From 7b6ddcf530edf8e04fe66c0a6a0140abe5b93f0a Mon Sep 17 00:00:00 2001 From: Tianyi Huang Date: Fri, 22 Nov 2024 16:44:16 -0800 Subject: [PATCH 1/6] Fact Validation Blogpost --- ...5-04-28-streamlining-factual-validation.md | 227 +++++++++++++++++ ...-04-28-streamlining-factual-validation.bib | 235 ++++++++++++++++++ .../Method.png | Bin 0 -> 504610 bytes 3 files changed, 462 insertions(+) create mode 100644 _posts/2025-04-28-streamlining-factual-validation.md create mode 100644 assets/bibliography/2025-04-28-streamlining-factual-validation.bib create mode 100644 assets/img/2025-04-28-streamlining-factual-validation/Method.png diff --git a/_posts/2025-04-28-streamlining-factual-validation.md b/_posts/2025-04-28-streamlining-factual-validation.md new file mode 100644 index 00000000..691d98e7 --- /dev/null +++ b/_posts/2025-04-28-streamlining-factual-validation.md @@ -0,0 +1,227 @@ +--- +layout: distill +title: "Streamlining Factual Validation: A Scalable Method to Enhance GPT Trustworthiness and Efficiency" +description: As the deployment of Large Language Models (LLMs) like GPT expands across domains, addressing their susceptibility to factual inaccuracies, or hallucinations, is core for reliable performance. In this blog post, we introduce a novel framework that enhances retrieval-augmented generation (RAG) pipelines by integrating LLM summarization, DBSCAN clustering, and vectorized fact storage, achieving a 57.7% reduction in storage size while maintaining near-parity in factual and retrieval accuracy on the PubmedQA dataset. By optimizing the intersection of efficiency, accuracy, and scalability, this framework contributes to the broader goal of advancing trustworthy AI for impactful real-world use cases. +date: 2025-04-28 +future: true +htmlwidgets: true +hidden: false + +authors: + - name: Anonymous + +bibliography: 2025-04-28-streamlining-factual-validation.bib + +toc: + - name: Introduction + - name: The Challenge of Factual Accuracy in AI + - name: Limitations of Existing Approaches + - name: Theoretical Foundations + - name: "Our Proposal: Fact-Based Validation for GPT Systems" + - name: Experimentation and Results + - name: Broader Implications + - name: Limitations and Future Work + - name: Conclusion + +_styles: > + .fake-img { + background: #bbb; + border: 1px solid rgba(0, 0, 0, 0.1); + box-shadow: 0 0px 4px rgba(0, 0, 0, 0.1); + margin-bottom: 12px; + } + .fake-img p { + font-family: monospace; + color: white; + text-align: left; + margin: 12px 0; + text-align: center; + font-size: 16px; + } +--- + +## Introduction +Large Language Models (LLMs), such as GPT, have advanced natural language processing by offering incredible fluency and adaptability . Yet, these models are susceptible to "hallucinations"---outputs that are linguistically coherent but factually incorrect . This issue arises because LLMs optimize for contextual fluency rather than accuracy, a trade-off that becomes increasingly problematic in high-stakes applications such as medicine, where precision is necessary . + +Existing solutions, including fine-tuning, retrieval-augmented generation (RAG), and post-hoc validation, address specific aspects of this problem but often at the cost of scalability or computational efficiency . These methods frequently fail to account for the growing demand to process unstructured data efficiently while maintaining factual accuracy. + +In this blog post, we explore an enhancement to RAG pipelines that combines summarization, clustering with DBSCAN, and vectorized fact storage to address challenges in managing unstructured data . By processing expansive datasets into concise, verifiable formats, this approach optimizes storage efficiency while preserving factual accuracy. Evaluated on the PubmedQA dataset, our framework demonstrates how preprocessing can streamline workflows and improve scalability without compromising retrieval precision, offering new possibilities for real-world applications . + +## The Challenge of Factual Accuracy in AI +### Hallucinations and Trust Deficit +Hallucinations in LLMs arise from their probabilistic design, which prioritizes predicting plausible sequences over verifying factual accuracy . This limitation is magnified by two interconnected factors: + +- **Dynamic and Unstructured Contexts**: LLMs are trained on vast datasets often lacking clear structure or schema. Additionally, knowledge evolves---facts that were accurate during training may become outdated, especially in rapidly advancing fields. Without mechanisms to adapt, models perpetuate inaccuracies . +- **Compounded Errors in Reasoning**: When initial premises are flawed, LLMs often propagate these errors across multi-step reasoning tasks, amplifying inaccuracies in their outputs . This cascading effect is particularly problematic for complex queries requiring precise, interdependent answers . + +These issues are not only technical inconveniences but fundamental barriers to deploying LLMs in sensitive domains. For instance, incorrect treatment suggestions in medical applications or fabricated legal citations could lead to severe consequences . + +## Limitations of Existing Approaches +### Fine-Tuning +Fine-tuning involves retraining LLMs on domain-specific datasets, improving their factual accuracy within specific contexts . However, it has inherent trade-offs: + +- High resource requirements make it impractical for many organizations and individuals . +- Static knowledge freezes models in time, necessitating constant retraining to stay relevant . +- Specialization often comes at the expense of general-purpose utility, limiting versatility . + +### Retrieval-Augmented Generation (RAG) +RAG pipelines integrate external knowledge retrieval into the LLM workflow, providing factual grounding by leveraging curated databases or search engines . While effective in many cases, RAG systems face challenges: + +- **Data Dependence**: Retrieved information may be incomplete, outdated, or biased, directly affecting output reliability . +- **Validation Gap**: RAG systems retrieve information but do not inherently validate it, leaving unresolved conflicts between retrieved facts and the LLM’s internal predictions . +- **Scalability**: As query complexity increases, retrieved information can become overly sparse, and the increasing contextual length can diminish its enhancement for the LLM, ultimately restricting scalability . + +### Post-Hoc Validation +Post-hoc validation attempts to correct inaccuracies after generation by cross-referencing outputs with external datasets or models . While valuable, it struggles from inefficiencies: + +- It introduces latency, making it unsuitable for real-time applications. +- Validating every output, even when unnecessary, wastes computational resources . +- It provides limited feedback for refining the underlying generative process, addressing symptoms but not root causes . + +## Theoretical Foundations +### Factual Validation: Core Principles +Factual validation ensures that LLM outputs are verifiable and contextually accurate. This approach focuses on: + +- **Granularity**: Decomposing responses into short factual units that allows for precise validation at the component level, reducing the likelihood of undetected inaccuracies . +- **Scalability**: By leveraging vectorized representations, validation systems can efficiently compare outputs against large, structured knowledge bases . + +### The Role of Vectorization +Vectorization encodes atomic facts into high-dimensional embeddings, facilitating efficient storage, retrieval, and comparison . This method allows the system to: + +- Quickly identify semantically relevant information from knowledge bases . +- Enable context-aware validation that adapts to the specific demands of a query . + +## Our Proposal: Fact-Based Validation for GPT Systems +To address the challenges of ensuring factual accuracy in GPT systems, we propose a framework that preprocesses data into structured and verifiable units with the aim to enhance the reliability of LLM-generated responses while optimizing storage and retrieval efficiency. + +
+ +
This diagram illustrates the workflow of the LLM validity judger. The process involves simplifying the LLM response into facts, fetching nearby facts from a vector database, and assessing the validity of the response based on this context. It then generates an overall validity score with an explanation. The diagram also illustrates a separate system called “New Data Input System” for inputting new data into the vector database, which operates independently from the validity judgment process. The fetching system depicted is the same as existing vector database fetching systems, simply shown for clarity.
+
+ +### System Architecture +- **New Data Input System**: Fact Extraction and Storage + - **Input**: Multiple text chunks containing factual information. + - **Output**: Extracted facts embedded as vector representations, stored in a vector database. + +- **LLM Validity Judger**: Prompt and Response Validation + - **Input**: A GPT prompt and its corresponding response. + - **Output**: A validity rating between 0 and 1 indicating the accuracy of the prompt/response pair, along with optional reasoning. + +## Implementation Details + +### New Data Input System + +**Fetching Contexts**: All contexts are fetched from the PubmedQA dataset to serve as the input data for preprocessing . + +**Summarization and Compression**: Each context is summarized using GPT-4o-mini, compressing the content into concise paragraphs. This step ensures efficient downstream processing and storage . + +**Embedding with OpenAI Models**: The summarized contexts are embedded into high-dimensional vector representations using OpenAI's text-embedding-3-large model . + +**Clustering with DBSCAN**: The embeddings are grouped into clusters using DBSCAN, which identifies similar contexts without requiring predefined categories. This step is critical for organizing related data efficiently . + +**Context Combination and Summarization**: Context summaries within each cluster are combined and re-summarized into unified representations using GPT-4o-mini, further reducing redundancy and optimizing coherence . + +**Final Embedding and Storage**: The new summaries are re-embedded with text-embedding-3-large . These embeddings, along with any unclustered individual summaries, are stored in PineconeDB for retrieval . + +### LLM Validity Judger + +**Decomposing Responses**: GPT processes the prompt and its corresponding response, breaking them into discrete factual statements. Each statement is assigned an importance score (0–1), indicating its relevance to the prompt. + +**Proximity Search**: For each statement, the system retrieves five of the closest facts from the vector database through a semantic proximity search . These facts serve as the context for validation. + +**Validity Scoring**: Each statement is evaluated against its contextual facts using GPT, which assigns a validity score between 0 and 1 based on alignment. This evaluation determines how well the statement aligns with retrieved facts . + +**Weighted Average Calculation**: The validity scores are aggregated into a weighted average, where the weights are derived from the importance scores of the statements. This weighted score represents the overall factual accuracy of the response. + +**Explainability**: If required, GPT generates a rationale for the validity rating, outlining which facts were used, how alignment was determined, and any potential conflicts. This reasoning enhances interpretability and transparency. + +### Why Not KNN for Clustering? + +K-nearest neighbor (KNN) was considered for fact clustering but was ultimately not used due to its reliance on predefined parameters, such as the number of clusters or neighbors—constraints that are impractical for real-world datasets with high diversity . In contrast, DBSCAN identifies clusters based on density, eliminating the need for prior knowledge of the dataset's structure . This makes DBSCAN more effective for processing unstructured or unknown data distributions. Additionally, KNN's approach risks significant data abstraction or loss when attempting to condense diverse facts into singular representations, worsening the system's ability to maintain factual granularity . + +## Additional Note + +If the dataset is preprocessed to isolate one specific fact per entry, we recommend bypassing that step. Instead, the data can directly enter our model, which dynamically groups related facts. This approach preserves the richness of the dataset and streamlines the overall workflow. + +## Advantages of This System + +- **Granular Verification**: Validates individual facts for higher precision compared to document- or paragraph-level assessments. +- **Simple Integration**: This system can be integrated into existing RAG infrastructure to enhance the overall functionality without requiring infrastructure changes. +- **Efficient Storage**: Compresses data through summarization and clustering, reducing storage needs and retrieval token usage. +- **Fact-Level Accuracy**: Ensures validity by requiring agreement across multiple sources before storing facts. +- **Real-Time Validation**: Enables fast, efficient validation within the generation process. +- **Scalable**: Modular design scales across datasets and domains, ensuring long-term applicability. + +## Experimentation and Results + +We evaluated our proposed pipeline by benchmarking it against the Traditional Retrieval-Augmented Generation (RAG) Pipeline using the PubmedQA dataset . The evaluation focused on three key metrics: factual accuracy, RAG effectiveness, and storage efficiency, collectively measuring response quality, retrieval precision, and data storage optimization. + +### Traditional RAG Pipeline + +The traditional RAG pipeline was tested under ideal conditions, embedding and retrieving the labeled contexts directly from the PubmedQA dataset . This setup provided perfect access to the correct answer contexts, offering a significant advantage. Despite this, our proposed pipeline—which applies summarization and compression—demonstrates performance comparable to this baseline, underscoring its effectiveness. + +The traditional pipeline workflow includes the following steps: +- Fetch all contexts from the PubmedQA dataset . +- Embed each context using OpenAI's `text-embedding-3-large` model. +- Store the embeddings in PineconeDB for retrieval. + +### Comparative Metrics + +| **Metric** | **Traditional Pipeline** | **Proposed Pipeline** | **Difference** | +|-----------------------|--------------------------|------------------------|---------------------------| +| Factual Accuracy | 71.7% | 71.2% | -0.5% | +| RAG Effectiveness | 99.2% | 98.9% | -0.3% | +| Storage Efficiency | 1,351 KB | 571 KB | -57.7% (Reduction) | + +**Table 1.** Comparison of factual accuracy, RAG effectiveness, and storage efficiency between the traditional pipeline and the proposed pipeline. The proposed pipeline achieves comparable performance in accuracy and effectiveness while significantly reducing storage requirements by 57.7%. + +### Factual Accuracy + +We tested factual accuracy to measure how well the system addressed prompts by integrating all processes, including context retrieval, summarization, and LLM response generation. Using the PubmedQA dataset with 1,000 labeled context-question-answer groups, we queried each question and deemed responses correct if they matched the dataset’s answer (yes, no, or maybe) . The traditional pipeline achieved 71.7% accuracy, while our pipeline achieved 71.2%, a negligible difference of 0.5%. This suggests that summarizing contexts did not hinder the LLM’s ability to generate correct answers. Further improvements in prompt engineering or summary generation could potentially surpass the traditional pipeline’s accuracy. + +### RAG Effectiveness + +RAG effectiveness was evaluated to determine how well each pipeline retrieved the most relevant context for a given query. The PubmedQA dataset contexts were queried using labeled questions, and a retrieval was marked correct if the top result matched the labeled correct context . The traditional pipeline achieved 99.2% RAG effectiveness, while our pipeline achieved 98.9%, a minor 0.3% difference. The minimal reduction shows that summarization and clustering do not compromise retrieval quality. Additionally, granting the proposed pipeline access to original embeddings instead of summaries for labeling could eliminate this gap entirely, further reinforcing its effectiveness. + +### Storage Efficiency + +We measured storage efficiency by calculating the total size of stored contexts in the vector database (excluding vector embeddings). The traditional pipeline required 1,351 KB, whereas our pipeline used only 571 KB, a reduction of 57.7%. This demonstrates the significant compression achieved through summarization and clustering. The benefits are particularly pronounced in unstructured datasets, where redundant data can be consolidated. While PubmedQA is a structured dataset, in more diverse real-world datasets, the proposed pipeline would likely achieve even greater storage savings. This reduced footprint allows for the storage of larger datasets and faster query times, providing scalability without sacrificing usability. + +## Broader Implications + +The results of this pipeline highlight its potential to advance how RAG systems handle unstructured and large-scale datasets. Its ability to compress and organize data effectively expands the capacity of vector databases, enabling systems to manage larger and more diverse datasets without sacrificing query performance. This scalability is critical for real-time applications and resource-constrained environments like edge computing . + +The modular design ensures easy integration with existing RAG systems, supporting seamless adoption while allowing for domain-specific customization. By balancing efficiency, scalability, and accuracy, this framework contributes to more practical and reliable AI systems. + +## Limitations and Future Work + +1. **Suboptimal Context Fit**: +- The data retrieved may not always align perfectly with the prompt, leading to potential mismatches in utility. +- **Mitigation**: Introduce additional retrieval steps that identify and remove irrelevant facts, guided by LLM evaluations. + +2. **Validity Scoring Challenges**: +- Validity ratings based on retrieved facts may lack precision due to ambiguous LLM prompts or insufficient contextual grounding. +- **Mitigation**: Provide scoring examples during prompting or fine-tune the LLM to compute scores more reliably from source data. + +3. **Context Sensitivity Issues**: +- Facts may be accurate in isolation but misleading when applied in certain contexts, limiting the system's adaptability. +- **Mitigation**: Extend vector embeddings to include source and context information, enabling the system to incorporate situational nuances. + +4. **Potential Source Bias**: +- Bias in the original source documents can propagate into the system, affecting the impartiality of responses. +- **Mitigation**: Add a preprocessing step to evaluate and mitigate biases in source data before vectorization. + +5. **Scope of Accuracy**: +- The system prioritizes factual accuracy without focusing on other dimensions of response quality, such as creative inference or prompt analysis. +- **Mitigation**: Include additional validity metrics, such as evaluating inferential accuracy or alignment with the original prompt, using separate GPT evaluations. + +6. **Expanded Dataset Evaluation**: +- Due to time constraints, the system was tested only on PubmedQA and did not undergo evaluation on a broader range of datasets. This limitation restricts the full assessment of the method’s scalability and effectiveness across all data types. +- **Mitigation**: Future work aims on expanding the evaluation to include additional benchmark datasets, such as SimpleQA . Sources like Wikipedia pages and news articles will be scraped and processed into the vector database to better assess the pipeline's performance under real-world conditions. + +## Conclusion + +In this blog post, we explored a novel framework for enhancing the efficiency and scalability of RAG pipelines while maintaining high standards of factual accuracy. By incorporating techniques such as summarization, clustering, and vectorized fact storage, we demonstrated how preprocessing can substantially reduce storage and optimize context management. + +Our experimentation with the PubmedQA dataset highlighted the pipeline’s ability to perform competitively with traditional methods with ideal access to labeled contexts. 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a/_posts/2025-04-28-streamlining-factual-validation.md +++ b/_posts/2025-04-28-streamlining-factual-validation.md @@ -41,55 +41,64 @@ _styles: > --- ## Introduction -Large Language Models (LLMs), such as GPT, have advanced natural language processing by offering incredible fluency and adaptability . Yet, these models are susceptible to "hallucinations"---outputs that are linguistically coherent but factually incorrect . This issue arises because LLMs optimize for contextual fluency rather than accuracy, a trade-off that becomes increasingly problematic in high-stakes applications such as medicine, where precision is necessary . +Large Language Models (LLMs), such as GPT, have advanced natural language processing by offering incredible fluency and adaptability . Yet, these models are susceptible to "hallucinations"---outputs that are linguistically coherent but factually incorrect . This issue arises because LLMs optimize for contextual fluency rather than accuracy, a trade-off that becomes increasingly problematic in high-stakes applications such as medicine, where precision is necessary . -Existing solutions, including fine-tuning, retrieval-augmented generation (RAG), and post-hoc validation, address specific aspects of this problem but often at the cost of scalability or computational efficiency . These methods frequently fail to account for the growing demand to process unstructured data efficiently while maintaining factual accuracy. +Existing solutions, including fine-tuning, retrieval-augmented generation (RAG), and post-hoc validation, address specific aspects of this problem but often at the cost of scalability or computational efficiency . These methods frequently fail to account for the growing demand to process unstructured data efficiently while maintaining factual accuracy. -In this blog post, we explore an enhancement to RAG pipelines that combines summarization, clustering with DBSCAN, and vectorized fact storage to address challenges in managing unstructured data . By processing expansive datasets into concise, verifiable formats, this approach optimizes storage efficiency while preserving factual accuracy. Evaluated on the PubmedQA dataset, our framework demonstrates how preprocessing can streamline workflows and improve scalability without compromising retrieval precision, offering new possibilities for real-world applications . +In this blog post, we explore an enhancement to RAG pipelines that combines summarization, clustering with DBSCAN, and vectorized fact storage to address challenges in managing unstructured data . By processing expansive datasets into concise, verifiable formats, this approach optimizes storage efficiency while preserving factual accuracy. Evaluated on the PubmedQA dataset, our framework demonstrates how preprocessing can streamline workflows and improve scalability without compromising retrieval precision, offering new possibilities for real-world applications . ## The Challenge of Factual Accuracy in AI -### Hallucinations and Trust Deficit -Hallucinations in LLMs arise from their probabilistic design, which prioritizes predicting plausible sequences over verifying factual accuracy . This limitation is magnified by two interconnected factors: -- **Dynamic and Unstructured Contexts**: LLMs are trained on vast datasets often lacking clear structure or schema. Additionally, knowledge evolves---facts that were accurate during training may become outdated, especially in rapidly advancing fields. Without mechanisms to adapt, models perpetuate inaccuracies . -- **Compounded Errors in Reasoning**: When initial premises are flawed, LLMs often propagate these errors across multi-step reasoning tasks, amplifying inaccuracies in their outputs . This cascading effect is particularly problematic for complex queries requiring precise, interdependent answers . +Hallucinations and Trust Deficit -These issues are not only technical inconveniences but fundamental barriers to deploying LLMs in sensitive domains. For instance, incorrect treatment suggestions in medical applications or fabricated legal citations could lead to severe consequences . +Hallucinations in LLMs arise from their probabilistic design, which prioritizes predicting plausible sequences over verifying factual accuracy . This limitation is magnified by two interconnected factors: + +- **Dynamic and Unstructured Contexts**: LLMs are trained on vast datasets often lacking clear structure or schema. Additionally, knowledge evolves---facts that were accurate during training may become outdated, especially in rapidly advancing fields. Without mechanisms to adapt, models perpetuate inaccuracies . +- **Compounded Errors in Reasoning**: When initial premises are flawed, LLMs often propagate these errors across multi-step reasoning tasks, amplifying inaccuracies in their outputs . This cascading effect is particularly problematic for complex queries requiring precise, interdependent answers . + +These issues are not only technical inconveniences but fundamental barriers to deploying LLMs in sensitive domains. For instance, incorrect treatment suggestions in medical applications or fabricated legal citations could lead to severe consequences . ## Limitations of Existing Approaches -### Fine-Tuning -Fine-tuning involves retraining LLMs on domain-specific datasets, improving their factual accuracy within specific contexts . However, it has inherent trade-offs: -- High resource requirements make it impractical for many organizations and individuals . -- Static knowledge freezes models in time, necessitating constant retraining to stay relevant . -- Specialization often comes at the expense of general-purpose utility, limiting versatility . +Fine-Tuning + +Fine-tuning involves retraining LLMs on domain-specific datasets, improving their factual accuracy within specific contexts . However, it has inherent trade-offs: + +- High resource requirements make it impractical for many organizations and individuals . +- Static knowledge freezes models in time, necessitating constant retraining to stay relevant . +- Specialization often comes at the expense of general-purpose utility, limiting versatility . + +Retrieval-Augmented Generation (RAG) + +RAG pipelines integrate external knowledge retrieval into the LLM workflow, providing factual grounding by leveraging curated databases or search engines . While effective in many cases, RAG systems face challenges: -### Retrieval-Augmented Generation (RAG) -RAG pipelines integrate external knowledge retrieval into the LLM workflow, providing factual grounding by leveraging curated databases or search engines . While effective in many cases, RAG systems face challenges: +- **Data Dependence**: Retrieved information may be incomplete, outdated, or biased, directly affecting output reliability . +- **Validation Gap**: RAG systems retrieve information but do not inherently validate it, leaving unresolved conflicts between retrieved facts and the LLM’s internal predictions . +- **Scalability**: As query complexity increases, retrieved information can become overly sparse, and the increasing contextual length can diminish its enhancement for the LLM, ultimately restricting scalability . -- **Data Dependence**: Retrieved information may be incomplete, outdated, or biased, directly affecting output reliability . -- **Validation Gap**: RAG systems retrieve information but do not inherently validate it, leaving unresolved conflicts between retrieved facts and the LLM’s internal predictions . -- **Scalability**: As query complexity increases, retrieved information can become overly sparse, and the increasing contextual length can diminish its enhancement for the LLM, ultimately restricting scalability . +Post-Hoc Validation -### Post-Hoc Validation -Post-hoc validation attempts to correct inaccuracies after generation by cross-referencing outputs with external datasets or models . While valuable, it struggles from inefficiencies: +Post-hoc validation attempts to correct inaccuracies after generation by cross-referencing outputs with external datasets or models . While valuable, it struggles from inefficiencies: - It introduces latency, making it unsuitable for real-time applications. -- Validating every output, even when unnecessary, wastes computational resources . -- It provides limited feedback for refining the underlying generative process, addressing symptoms but not root causes . +- Validating every output, even when unnecessary, wastes computational resources . +- It provides limited feedback for refining the underlying generative process, addressing symptoms but not root causes . ## Theoretical Foundations -### Factual Validation: Core Principles + +Factual Validation: Core Principles + Factual validation ensures that LLM outputs are verifiable and contextually accurate. This approach focuses on: -- **Granularity**: Decomposing responses into short factual units that allows for precise validation at the component level, reducing the likelihood of undetected inaccuracies . -- **Scalability**: By leveraging vectorized representations, validation systems can efficiently compare outputs against large, structured knowledge bases . +- **Granularity**: Decomposing responses into short factual units that allows for precise validation at the component level, reducing the likelihood of undetected inaccuracies . +- **Scalability**: By leveraging vectorized representations, validation systems can efficiently compare outputs against large, structured knowledge bases . + +The Role of Vectorization -### The Role of Vectorization -Vectorization encodes atomic facts into high-dimensional embeddings, facilitating efficient storage, retrieval, and comparison . This method allows the system to: +Vectorization encodes atomic facts into high-dimensional embeddings, facilitating efficient storage, retrieval, and comparison . This method allows the system to: -- Quickly identify semantically relevant information from knowledge bases . -- Enable context-aware validation that adapts to the specific demands of a query . +- Quickly identify semantically relevant information from knowledge bases . +- Enable context-aware validation that adapts to the specific demands of a query . ## Our Proposal: Fact-Based Validation for GPT Systems To address the challenges of ensuring factual accuracy in GPT systems, we propose a framework that preprocesses data into structured and verifiable units with the aim to enhance the reliability of LLM-generated responses while optimizing storage and retrieval efficiency. @@ -99,7 +108,8 @@ To address the challenges of ensuring factual accuracy in GPT systems, we propos

This diagram illustrates the workflow of the LLM validity judger. The process involves simplifying the LLM response into facts, fetching nearby facts from a vector database, and assessing the validity of the response based on this context. It then generates an overall validity score with an explanation. The diagram also illustrates a separate system called “New Data Input System” for inputting new data into the vector database, which operates independently from the validity judgment process. The fetching system depicted is the same as existing vector database fetching systems, simply shown for clarity.
-### System Architecture +System Architecture + - **New Data Input System**: Fact Extraction and Storage - **Input**: Multiple text chunks containing factual information. - **Output**: Extracted facts embedded as vector representations, stored in a vector database. @@ -109,38 +119,37 @@ To address the challenges of ensuring factual accuracy in GPT systems, we propos - **Output**: A validity rating between 0 and 1 indicating the accuracy of the prompt/response pair, along with optional reasoning. ## Implementation Details +New Data Input System -### New Data Input System - -**Fetching Contexts**: All contexts are fetched from the PubmedQA dataset to serve as the input data for preprocessing . +**Fetching Contexts**: All contexts are fetched from the PubmedQA dataset to serve as the input data for preprocessing . **Summarization and Compression**: Each context is summarized using GPT-4o-mini, compressing the content into concise paragraphs. This step ensures efficient downstream processing and storage . **Embedding with OpenAI Models**: The summarized contexts are embedded into high-dimensional vector representations using OpenAI's text-embedding-3-large model . -**Clustering with DBSCAN**: The embeddings are grouped into clusters using DBSCAN, which identifies similar contexts without requiring predefined categories. This step is critical for organizing related data efficiently . +**Clustering with DBSCAN**: The embeddings are grouped into clusters using DBSCAN, which identifies similar contexts without requiring predefined categories. This step is critical for organizing related data efficiently . **Context Combination and Summarization**: Context summaries within each cluster are combined and re-summarized into unified representations using GPT-4o-mini, further reducing redundancy and optimizing coherence . **Final Embedding and Storage**: The new summaries are re-embedded with text-embedding-3-large . These embeddings, along with any unclustered individual summaries, are stored in PineconeDB for retrieval . -### LLM Validity Judger +LLM Validity Judger **Decomposing Responses**: GPT processes the prompt and its corresponding response, breaking them into discrete factual statements. Each statement is assigned an importance score (0–1), indicating its relevance to the prompt. -**Proximity Search**: For each statement, the system retrieves five of the closest facts from the vector database through a semantic proximity search . These facts serve as the context for validation. +**Proximity Search**: For each statement, the system retrieves five of the closest facts from the vector database through a semantic proximity search . These facts serve as the context for validation. -**Validity Scoring**: Each statement is evaluated against its contextual facts using GPT, which assigns a validity score between 0 and 1 based on alignment. This evaluation determines how well the statement aligns with retrieved facts . +**Validity Scoring**: Each statement is evaluated against its contextual facts using GPT, which assigns a validity score between 0 and 1 based on alignment. This evaluation determines how well the statement aligns with retrieved facts . **Weighted Average Calculation**: The validity scores are aggregated into a weighted average, where the weights are derived from the importance scores of the statements. This weighted score represents the overall factual accuracy of the response. **Explainability**: If required, GPT generates a rationale for the validity rating, outlining which facts were used, how alignment was determined, and any potential conflicts. This reasoning enhances interpretability and transparency. -### Why Not KNN for Clustering? +Why Not KNN for Clustering? -K-nearest neighbor (KNN) was considered for fact clustering but was ultimately not used due to its reliance on predefined parameters, such as the number of clusters or neighbors—constraints that are impractical for real-world datasets with high diversity . In contrast, DBSCAN identifies clusters based on density, eliminating the need for prior knowledge of the dataset's structure . This makes DBSCAN more effective for processing unstructured or unknown data distributions. Additionally, KNN's approach risks significant data abstraction or loss when attempting to condense diverse facts into singular representations, worsening the system's ability to maintain factual granularity . +K-nearest neighbor (KNN) was considered for fact clustering but was ultimately not used due to its reliance on predefined parameters, such as the number of clusters or neighbors—constraints that are impractical for real-world datasets with high diversity . In contrast, DBSCAN identifies clusters based on density, eliminating the need for prior knowledge of the dataset's structure . This makes DBSCAN more effective for processing unstructured or unknown data distributions. Additionally, KNN's approach risks significant data abstraction or loss when attempting to condense diverse facts into singular representations, worsening the system's ability to maintain factual granularity . -## Additional Note +Additional Note If the dataset is preprocessed to isolate one specific fact per entry, we recommend bypassing that step. Instead, the data can directly enter our model, which dynamically groups related facts. This approach preserves the richness of the dataset and streamlines the overall workflow. @@ -155,18 +164,18 @@ If the dataset is preprocessed to isolate one specific fact per entry, we recomm ## Experimentation and Results -We evaluated our proposed pipeline by benchmarking it against the Traditional Retrieval-Augmented Generation (RAG) Pipeline using the PubmedQA dataset . The evaluation focused on three key metrics: factual accuracy, RAG effectiveness, and storage efficiency, collectively measuring response quality, retrieval precision, and data storage optimization. +We evaluated our proposed pipeline by benchmarking it against the Traditional Retrieval-Augmented Generation (RAG) Pipeline using the PubmedQA dataset . The evaluation focused on three key metrics: factual accuracy, RAG effectiveness, and storage efficiency, collectively measuring response quality, retrieval precision, and data storage optimization. -### Traditional RAG Pipeline +Traditional RAG Pipeline -The traditional RAG pipeline was tested under ideal conditions, embedding and retrieving the labeled contexts directly from the PubmedQA dataset . This setup provided perfect access to the correct answer contexts, offering a significant advantage. Despite this, our proposed pipeline—which applies summarization and compression—demonstrates performance comparable to this baseline, underscoring its effectiveness. +The traditional RAG pipeline was tested under ideal conditions, embedding and retrieving the labeled contexts directly from the PubmedQA dataset . This setup provided perfect access to the correct answer contexts, offering a significant advantage. Despite this, our proposed pipeline—which applies summarization and compression—demonstrates performance comparable to this baseline, underscoring its effectiveness. The traditional pipeline workflow includes the following steps: -- Fetch all contexts from the PubmedQA dataset . +- Fetch all contexts from the PubmedQA dataset . - Embed each context using OpenAI's `text-embedding-3-large` model. - Store the embeddings in PineconeDB for retrieval. -### Comparative Metrics +Comparative Metrics | **Metric** | **Traditional Pipeline** | **Proposed Pipeline** | **Difference** | |-----------------------|--------------------------|------------------------|---------------------------| @@ -176,21 +185,21 @@ The traditional pipeline workflow includes the following steps: **Table 1.** Comparison of factual accuracy, RAG effectiveness, and storage efficiency between the traditional pipeline and the proposed pipeline. The proposed pipeline achieves comparable performance in accuracy and effectiveness while significantly reducing storage requirements by 57.7%. -### Factual Accuracy +Factual Accuracy -We tested factual accuracy to measure how well the system addressed prompts by integrating all processes, including context retrieval, summarization, and LLM response generation. Using the PubmedQA dataset with 1,000 labeled context-question-answer groups, we queried each question and deemed responses correct if they matched the dataset’s answer (yes, no, or maybe) . The traditional pipeline achieved 71.7% accuracy, while our pipeline achieved 71.2%, a negligible difference of 0.5%. This suggests that summarizing contexts did not hinder the LLM’s ability to generate correct answers. Further improvements in prompt engineering or summary generation could potentially surpass the traditional pipeline’s accuracy. +We tested factual accuracy to measure how well the system addressed prompts by integrating all processes, including context retrieval, summarization, and LLM response generation. Using the PubmedQA dataset with 1,000 labeled context-question-answer groups, we queried each question and deemed responses correct if they matched the dataset’s answer (yes, no, or maybe) . The traditional pipeline achieved 71.7% accuracy, while our pipeline achieved 71.2%, a negligible difference of 0.5%. This suggests that summarizing contexts did not hinder the LLM’s ability to generate correct answers. Further improvements in prompt engineering or summary generation could potentially surpass the traditional pipeline’s accuracy. -### RAG Effectiveness +RAG Effectiveness -RAG effectiveness was evaluated to determine how well each pipeline retrieved the most relevant context for a given query. The PubmedQA dataset contexts were queried using labeled questions, and a retrieval was marked correct if the top result matched the labeled correct context . The traditional pipeline achieved 99.2% RAG effectiveness, while our pipeline achieved 98.9%, a minor 0.3% difference. The minimal reduction shows that summarization and clustering do not compromise retrieval quality. Additionally, granting the proposed pipeline access to original embeddings instead of summaries for labeling could eliminate this gap entirely, further reinforcing its effectiveness. +RAG effectiveness was evaluated to determine how well each pipeline retrieved the most relevant context for a given query. The PubmedQA dataset contexts were queried using labeled questions, and a retrieval was marked correct if the top result matched the labeled correct context . The traditional pipeline achieved 99.2% RAG effectiveness, while our pipeline achieved 98.9%, a minor 0.3% difference. The minimal reduction shows that summarization and clustering do not compromise retrieval quality. Additionally, granting the proposed pipeline access to original embeddings instead of summaries for labeling could eliminate this gap entirely, further reinforcing its effectiveness. -### Storage Efficiency +Storage Efficiency We measured storage efficiency by calculating the total size of stored contexts in the vector database (excluding vector embeddings). The traditional pipeline required 1,351 KB, whereas our pipeline used only 571 KB, a reduction of 57.7%. This demonstrates the significant compression achieved through summarization and clustering. The benefits are particularly pronounced in unstructured datasets, where redundant data can be consolidated. While PubmedQA is a structured dataset, in more diverse real-world datasets, the proposed pipeline would likely achieve even greater storage savings. This reduced footprint allows for the storage of larger datasets and faster query times, providing scalability without sacrificing usability. ## Broader Implications -The results of this pipeline highlight its potential to advance how RAG systems handle unstructured and large-scale datasets. Its ability to compress and organize data effectively expands the capacity of vector databases, enabling systems to manage larger and more diverse datasets without sacrificing query performance. This scalability is critical for real-time applications and resource-constrained environments like edge computing . +The results of this pipeline highlight its potential to advance how RAG systems handle unstructured and large-scale datasets. Its ability to compress and organize data effectively expands the capacity of vector databases, enabling systems to manage larger and more diverse datasets without sacrificing query performance. This scalability is critical for real-time applications and resource-constrained environments like edge computing . The modular design ensures easy integration with existing RAG systems, supporting seamless adoption while allowing for domain-specific customization. By balancing efficiency, scalability, and accuracy, this framework contributes to more practical and reliable AI systems. @@ -218,7 +227,7 @@ The modular design ensures easy integration with existing RAG systems, supportin 6. **Expanded Dataset Evaluation**: - Due to time constraints, the system was tested only on PubmedQA and did not undergo evaluation on a broader range of datasets. This limitation restricts the full assessment of the method’s scalability and effectiveness across all data types. -- **Mitigation**: Future work aims on expanding the evaluation to include additional benchmark datasets, such as SimpleQA . Sources like Wikipedia pages and news articles will be scraped and processed into the vector database to better assess the pipeline's performance under real-world conditions. +- **Mitigation**: Future work aims on expanding the evaluation to include additional benchmark datasets, such as SimpleQA . Sources like Wikipedia pages and news articles will be scraped and processed into the vector database to better assess the pipeline's performance under real-world conditions. ## Conclusion diff --git a/assets/bibliography/2025-04-28-streamlining-factual-validation.bib b/assets/bibliography/2025-04-28-streamlining-factual-validation.bib index bd338601..5cd59742 100644 --- a/assets/bibliography/2025-04-28-streamlining-factual-validation.bib +++ b/assets/bibliography/2025-04-28-streamlining-factual-validation.bib @@ -5,28 +5,28 @@ @article{brown2020 year={2020} } -@article{Ji2022, +@article{ji2022, title={Survey of Hallucination in Natural Language Generation}, author={Ji, Ziwei and Lee, Nayeon and Frieske, Rita and Yu, Tiezheng and Su, Dan and Xu, Yan and Ishii, Etsuko and Bang, Yejin and Madotto, Andrea and Fung, Pascale}, journal={arXiv preprint arXiv:2202.03629}, year={2022} } -@article{Maynez2020, +@article{maynez2020, title={On Faithfulness and Factuality in Abstractive Summarization}, author={Maynez, Joshua and Narayan, Shashi and Bohnet, Bernd and McDonald, Ryan}, journal={arXiv preprint arXiv:2005.00661}, year={2020} } -@article{Holtzman2019, +@article{holtzman2019, title={The Curious Case of Neural Text Degeneration}, author={Holtzman, Ari and Buys, Jan and Du, Li and Forbes, Maxwell and Choi, Yejin}, journal={arXiv preprint arXiv:1904.09751}, year={2020} } -@article{Esteva2019, +@article{esteva2019, title={A Guide to Deep Learning in Healthcare}, author={Esteva, Andre and Robicquet, Alexandre and Ramsundar, Bharath and Kuleshov, Volodymyr and DePristo, Mark and Chou, Katherine and Cui, Claire and Corrado, Greg and Thrun, Sebastian and Dean, Jeff}, journal={Nature Medicine}, @@ -35,7 +35,7 @@ @article{Esteva2019 year={2019} } -@article{Ullah2024, +@article{ullah2024, title={Challenges and Barriers of Using Large Language Models (LLM) Such as ChatGPT for Diagnostic Medicine with a Focus on Digital Pathology – A Recent Scoping Review}, author={Ullah, Ehsan and Parwani, Anil and Baig, Mirza Mansoor and Singh, Rajendra}, journal={Diagnostic Pathology}, @@ -43,40 +43,40 @@ @article{Ullah2024 year={2024} } -@article{Guu2020, +@article{guu2020, title={REALM: Retrieval-Augmented Language Model Pre-Training}, author={Guu, Kelvin and Lee, Kenton and Tung, Zora and Pasupat, Panupong and Chang, Ming-Wei}, journal={arXiv preprint arXiv:2002.08909}, year={2020} } -@article{Ester1996, +@article{ester1996, title={A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise}, author={Ester, M. and Kriegel, H. and Sander, J{\"o}rg and Xu, Xiaowei}, year={1996} } -@article{Jin2019, +@article{jin2019, title={PubMedQA: A Dataset for Biomedical Research Question Answering}, author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William W. and Lu, Xinghua}, journal={arXiv preprint arXiv:1909.06146}, year={2019} } -@article{Radford2019, +@article{radford2019, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } -@article{Mousavi2024, +@article{mousavi2024, title={Is Your LLM Outdated? Benchmarking LLMs \& Alignment Algorithms for Time-Sensitive Knowledge}, author={Mousavi, Seyed Mahed and Alghisi, Simone and Riccardi, Giuseppe}, journal={arXiv preprint arXiv:2404.08700}, year={2024} } -@inproceedings{Bender2021, +@inproceedings{bender2021, title={On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?}, author={Bender, Emily and McMillan-Major, Angelina and Shmitchell, Shmargaret and Gebru, Timnit}, booktitle={FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency}, @@ -84,21 +84,21 @@ @inproceedings{Bender2021 year={2021} } -@article{Wei2022, +@article{wei2022, title={Chain of Thought Prompting Elicits Reasoning in Large Language Models}, author={Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and Ichter, Brian and Xia, Fei and Chi, Ed and Le, Quoc and Zhou, Denny}, journal={arXiv preprint arXiv:2201.11903}, year={2022} } -@article{Howard2018, +@article{howard2018, title={Universal Language Model Fine-tuning for Text Classification}, author={Howard, Jeremy and Ruder, Sebastian}, journal={arXiv preprint arXiv:1801.06146}, year={2018} } -@article{Zhang2022, +@article{zhang2022, title = {Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively}, author = {Zhang, Haojie and Li, Ge and Li, Jia and Zhang, Zhongjin and Zhu, Yuchuan and Jin, Zhi}, @@ -106,14 +106,14 @@ @article{Zhang2022 year = {2022} } -@article{Bommasani2021, +@article{bommasani2021, title={On the Opportunities and Risks of Foundation Models}, author={Bommasani, Rishi and Hudson, Drew A. and Adeli, Ehsan and Altman, Russ and Arora, Simran and von Arx, Sydney and Bernstein, Michael S. and Bohg, Jeannette and Bosselut, Antoine and Brunskill, Emma and Brynjolfsson, Erik and Buch, Shyamal and Card, Dallas and Castellon, Rodrigo and Chatterji, Niladri and Chen, Annie and Creel, Kathleen and Davis, Jared Quincy and Demszky, Dora and Donahue, Chris and Doumbouya, Moussa and Durmus, Esin and Ermon, Stefano and Etchemendy, John and Ethayarajh, Kawin and Fei-Fei, Li and Finn, Chelsea and Gale, Trevor and Gillespie, Lauren and Goel, Karan and Goodman, Noah and Grossman, Shelby and Guha, Neel and Hashimoto, Tatsunori and Henderson, Peter and Hewitt, John and Ho, Daniel E. and Hong, Jenny and Hsu, Kyle and Huang, Jing and Icard, Thomas and Jain, Saahil and Jurafsky, Dan and Kalluri, Pratyusha and Karamcheti, Siddharth and Keeling, Geoff and Khani, Fereshte and Khattab, Omar and Koh, Pang Wei and Krass, Mark and Krishna, Ranjay and Kuditipudi, Rohith and Kumar, Ananya and Ladhak, Faisal and Lee, Mina and Lee, Tony and Leskovec, Jure and Levent, Isabelle and Li, Xiang Lisa and Li, Xuechen and Ma, Tengyu and Malik, Ali and Manning, Christopher D. and Mirchandani, Suvir and Mitchell, Eric and Munyikwa, Zanele and Nair, Suraj and Narayan, Avanika and Narayanan, Deepak and Newman, Ben and Nie, Allen and Niebles, Juan Carlos and Nilforoshan, Hamed and Nyarko, Julian and Ogut, Giray and Orr, Laurel and Papadimitriou, Isabel and Park, Joon Sung and Piech, Chris and Portelance, Eva and Potts, Christopher and Raghunathan, Aditi and Reich, Rob and Ren, Hongyu and Rong, Frieda and Roohani, Yusuf and Ruiz, Camilo and Ryan, Jack and Ré, Christopher and Sadigh, Dorsa and Sagawa, Shiori and Santhanam, Keshav and Shih, Andy and Srinivasan, Krishnan and Tamkin, Alex and Taori, Rohan and Thomas, Armin W. and Tramèr, Florian and Wang, Rose E. and Wang, William and Wu, Bohan and Wu, Jiajun and Wu, Yuhuai and Xie, Sang Michael and Yasunaga, Michihiro and You, Jiaxuan and Zaharia, Matei and Zhang, Michael and Zhang, Tianyi and Zhang, Xikun and Zhang, Yuhui and Zheng, Lucia and Zhou, Kaitlyn and Liang, Percy}, journal={arXiv preprint arXiv:2108.07258}, year={2021} } -@inproceedings{Ruder2019, +@inproceedings{ruder2019, title={Transfer Learning in Natural Language Processing}, author={Ruder, Sebastian and Peters, Matthew E. and Swayamdipta, Swabha and Wolf, Thomas}, booktitle={Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials}, @@ -121,56 +121,56 @@ @inproceedings{Ruder2019 year={2019} } -@misc{IBM2023, +@misc{ibm2023, title={What is Retrieval-Augmented Generation?}, author={Martineau, Kim}, journal={IBM Research Blog}, year={2021} } -@article{Lewis2020, +@article{lewis2020, title={Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks}, author={Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and Küttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rocktäschel, Tim and Riedel, Sebastian and Kiela, Douwe}, journal={arXiv preprint arXiv:2005.11401}, year={2021} } -@article{Karpukhin2020, +@article{karpukhin2020, title={Dense Passage Retrieval for Open-Domain Question Answering}, author={Karpukhin, Vladimir and Oğuz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau}, journal={arXiv preprint arXiv:2004.04906}, year={2020} } -@article{Laban2024, +@article{laban2024, title={Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems}, author={Laban, Philippe and Fabbri, Alexander R and Xiong, Caiming and Wu, Chien-Sheng}, journal={arXiv preprint arXiv:2407.01370}, year={2024} } -@article{Zhong2024, +@article{zhong2024, title={Harnessing Large Language Models as Post-hoc Correctors}, author={Zhong, Zhiqiang and Zhou, Kuangyu and Mottin, Davide}, journal={arXiv preprint arXiv:2402.13414}, year={2024} } -@article{Li2024, +@article{li2024, title={Towards a Unified Language Model for Knowledge-Intensive Tasks Utilizing External Corpus}, author={Li, Xiaoxi and Dou, Zhicheng and Zhou, Yujia and Liu, Fangchao}, journal={arXiv preprint arXiv:2402.01176}, year={2024} } -@article{Johnson2017, +@article{johnson2017, title={Billion-scale Similarity Search with GPUs}, author={Johnson, Jeff and Douze, Matthijs and Jégou, Hervé}, journal={arXiv preprint arXiv:1702.08734}, year={2017} } -@article{Reimers2019, +@article{reimers2019, title={Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks}, author={Reimers, Nils and Gurevych, Iryna}, journal={arXiv preprint arXiv:1908.10084}, @@ -198,14 +198,14 @@ @misc{pinecone year={2024} } -@article{Kryscinski2019, +@article{kryscinski2019, title={Evaluating the Factual Consistency of Abstractive Text Summarization}, author={Kryściński, Wojciech and McCann, Bryan and Xiong, Caiming and Socher, Richard}, journal={arXiv preprint arXiv:1910.12840}, year={2019} } -@inproceedings{Taunk2019, +@inproceedings{taunk2019, title = {A Brief Review of Nearest Neighbor Algorithm for Learning and Classification}, author = {Taunk, K. and De, S. and Verma, S. and Swetapadma, A.}, booktitle={2019 International Conference on Intelligent Computing and Control Systems (ICCS)}, @@ -213,21 +213,21 @@ @inproceedings{Taunk2019 year={2019} } -@article{Cunningham2020, +@article{cunningham2020, title={k-Nearest Neighbour Classifiers: 2nd Edition (with Python Examples)}, author={Cunningham, Padraig and Delany, Sarah Jane}, journal={arXiv preprint arXiv:2004.04523}, year={2020} } -@article{Yao2024, +@article{yao2024, title={VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework}, author={Yao, Zhi and Tang, Zhiqing and Lou, Jiong and Shen, Ping and Jia, Weijia}, journal={arXiv preprint arXiv:2406.13399}, year={2024} } -@misc{simpleQA, +@misc{simpleqa, title={Introducing SimpleQA}, author={OpenAI}, journal={OpenAI Research}, From 6da6820bd544fc158e787966902bbac95563d142 Mon Sep 17 00:00:00 2001 From: Tianyi Huang Date: Sat, 23 Nov 2024 01:59:28 -0800 Subject: [PATCH 3/6] Update --- ...5-04-28-streamlining-factual-validation.md | 244 +++++++++++++++++- ...-04-28-streamlining-factual-validation.bib | 11 + .../Method.png | Bin 504610 -> 1065100 bytes .../finetuning.png | Bin 0 -> 201108 bytes .../hallucination.png | Bin 0 -> 973727 bytes .../posthoc.png | Bin 0 -> 181472 bytes .../rag.png | Bin 0 -> 184545 bytes 7 files changed, 241 insertions(+), 14 deletions(-) create mode 100644 assets/img/2025-04-28-streamlining-factual-validation/finetuning.png create mode 100644 assets/img/2025-04-28-streamlining-factual-validation/hallucination.png create mode 100644 assets/img/2025-04-28-streamlining-factual-validation/posthoc.png create mode 100644 assets/img/2025-04-28-streamlining-factual-validation/rag.png diff --git a/_posts/2025-04-28-streamlining-factual-validation.md b/_posts/2025-04-28-streamlining-factual-validation.md index a1497a5e..06debbff 100644 --- a/_posts/2025-04-28-streamlining-factual-validation.md +++ b/_posts/2025-04-28-streamlining-factual-validation.md @@ -38,10 +38,48 @@ _styles: > text-align: center; font-size: 16px; } + .box-important { + background: linear-gradient(135deg, #f8f9fa, #e6f7f9); /* Solid rgba(173, 216, 230, 0.2) */ + border: 1px solid #76b7c7; + padding: 20px; + margin: 20px 0; + box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); + font-family: 'Arial', sans-serif; + font-size: 16px; + color: #2a3e4b; + line-height: 1.7; + border-radius: 0px; + } + .matrix-box { + border: 1px solid #d3d3d3; + border-radius: 10px; + padding: 15px; + margin: 20px auto; + background-color: #fefefe; + box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); + display: inline-block; + } + summary-math { + text-align: center; + color: black; + } + [data-theme="dark"] summary-math { + color: white; + } + d-article { + overflow-x: visible; + } --- ## Introduction -Large Language Models (LLMs), such as GPT, have advanced natural language processing by offering incredible fluency and adaptability . Yet, these models are susceptible to "hallucinations"---outputs that are linguistically coherent but factually incorrect . This issue arises because LLMs optimize for contextual fluency rather than accuracy, a trade-off that becomes increasingly problematic in high-stakes applications such as medicine, where precision is necessary . +Large Language Models (LLMs), such as GPT, have advanced natural language processing by offering incredible fluency and adaptability . Yet, these models are susceptible to **"hallucinations"**---outputs that are linguistically coherent but factually incorrect . This issue arises because LLMs optimize for contextual fluency rather than accuracy, a trade-off that becomes increasingly problematic in applications where precision or reliability is necessary . + +
+ +
+ Figure 1. An example of hallucination in ChatGPT. This figure demonstrates a fabricated response confidently generated by ChatGPT, showing how large language models (LLMs) can produce coherent but factually incorrect outputs. The highlighted sections indicate fictional and misleading content, emphasizing the challenges in ensuring factual accuracy in LLM-generated responses. +
+
Existing solutions, including fine-tuning, retrieval-augmented generation (RAG), and post-hoc validation, address specific aspects of this problem but often at the cost of scalability or computational efficiency . These methods frequently fail to account for the growing demand to process unstructured data efficiently while maintaining factual accuracy. @@ -53,15 +91,82 @@ In this blog post, we explore an enhancement to RAG pipelines that combines summ Hallucinations in LLMs arise from their probabilistic design, which prioritizes predicting plausible sequences over verifying factual accuracy . This limitation is magnified by two interconnected factors: -- **Dynamic and Unstructured Contexts**: LLMs are trained on vast datasets often lacking clear structure or schema. Additionally, knowledge evolves---facts that were accurate during training may become outdated, especially in rapidly advancing fields. Without mechanisms to adapt, models perpetuate inaccuracies . -- **Compounded Errors in Reasoning**: When initial premises are flawed, LLMs often propagate these errors across multi-step reasoning tasks, amplifying inaccuracies in their outputs . This cascading effect is particularly problematic for complex queries requiring precise, interdependent answers . - -These issues are not only technical inconveniences but fundamental barriers to deploying LLMs in sensitive domains. For instance, incorrect treatment suggestions in medical applications or fabricated legal citations could lead to severe consequences . - +1. Dynamic and Unstructured Contexts + +LLMs are trained on vast corpora of text that often lack semantic organization. Moreover, the facts embedded within these datasets are static—reflecting the state of the world at the time of training. However, real-world knowledge evolves dynamically, leaving models unable to adapt without post-training updates . + +#### Formalizing the Knowledge Divergence +Let the model's knowledge at time $$t$$ be denoted as $$K(t)$$. We can express the knowledge state as: +$$ +K(t) = \textcolor{#5DADEC}{K(t_0)} + \textcolor{#E67E22}{\Delta K(t)}, +$$ +where: +- $$\textcolor{#5DADEC}{K(t_0)}$$ represents the knowledge at the time of training. +- $$\textcolor{#E67E22}{\Delta K(t)}$$ captures incremental updates post-deployment. + +In most deployed LLMs: +$$ +\textcolor{#E67E22}{\Delta K(t)} = 0, +$$ +indicating that the knowledge remains frozen at $$\textcolor{#5DADEC}{K(t_0)}$$. Over time, as $$t \to \infty$$, the divergence between $$K(t)$$ and real-world knowledge widens, resulting in increasingly outdated or inaccurate outputs. +
+#### Real-World Implications +This static nature is especially problematic in domains such as: + +- Medicine, where evolving treatment protocols and research are critical . +- Law, where changes in regulations or precedents can render older knowledge obsolete. + +Without mechanisms to dynamically update $$\Delta K(t)$$, LLMs fail to provide accurate, timely information in these high-stakes environments. +
+ +2. Compounded Errors in Reasoning + +In multi-step reasoning, LLMs encounter a **cascading error problem**, where inaccuracies at each step propagate and amplify in subsequent steps . + +#### Quantifying Error Propagation +Let $$P(x_n)$$ denote the probability of correctness at reasoning step $$n$$. The overall probability of correctness across $$N$$ steps is given by: +$$ +P_{\text{total}} = \prod_{n=1}^N \textcolor{#0f941b}{P(x_n)}. +$$ +Since $$\textcolor{#0f941b}{P(x_n)} < 1$$ in most real-world scenarios, $$P_{\text{total}}$$ decays exponentially as $$N$$ increases. This exponential decay illustrates how longer reasoning chains magnify errors, making LLMs less reliable for complex tasks . + +#### Modeling Error Interactions + +Error propagation can also be modeled using second-order interactions through the Hessian matrix : +$$ +H_{ij} = \frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_i \partial x_j}. +$$ + +To represent this interaction in matrix form, the Hessian matrix can be visualized as: + +
+$$ +H = +\begin{bmatrix} +\frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_1^2} & \frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_1 \partial x_2} & \cdots & \frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_1 \partial x_n} \\ +\frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_2 \partial x_1} & \frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_2^2} & \cdots & \frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_2 \partial x_n} \\ +\vdots & \vdots & \ddots & \vdots \\ +\frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_n \partial x_1} & \frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_n \partial x_2} & \cdots & \frac{\partial^2 \textcolor{#E74C3C}{E}}{\partial x_n^2} +\end{bmatrix} +$$ +
+ +where: +- $$H_{ij}$$ quantifies how an error in dimension $$x_i$$ interacts with errors in dimension $$x_j$$. +- $$\textcolor{#E74C3C}{E}$$ is the error function dependent on reasoning dimensions $$x_i$$. + +This matrix representation highlights the symmetry of the Hessian ($$H_{ij} = H_{ji}$$), demonstrating how second-order effects capture complex interactions between dimensions. By modeling these interactions, we can better understand how errors propagate and amplify through reasoning pathways. This indicates that inaccuracies are not isolated; instead, they cascade through interconnected reasoning pathways, compounding the overall error and making multi-step reasoning increasingly unreliable in LLMs. ## Limitations of Existing Approaches Fine-Tuning +
+ +
+ Figure 2. Workflow showing LLM fine-tuning with domain-specific data, integrating fine-tuned datasets to produce specialized responses. +
+
+ Fine-tuning involves retraining LLMs on domain-specific datasets, improving their factual accuracy within specific contexts . However, it has inherent trade-offs: - High resource requirements make it impractical for many organizations and individuals . @@ -70,6 +175,13 @@ Fine-tuning involves retraining LLMs on domain-specific datasets, improving thei Retrieval-Augmented Generation (RAG) +
+ +
+ Figure 3. Workflow showing external data retrieval and integration with the LLM to generate grounded responses. +
+
+ RAG pipelines integrate external knowledge retrieval into the LLM workflow, providing factual grounding by leveraging curated databases or search engines . While effective in many cases, RAG systems face challenges: - **Data Dependence**: Retrieved information may be incomplete, outdated, or biased, directly affecting output reliability . @@ -78,6 +190,13 @@ RAG pipelines integrate external knowledge retrieval into the LLM workflow, prov Post-Hoc Validation +
+ +
+ Figure 4. Workflow showing validation of LLM-generated responses against external datasets, with flagged issues reprocessed for accuracy. +
+
+ Post-hoc validation attempts to correct inaccuracies after generation by cross-referencing outputs with external datasets or models . While valuable, it struggles from inefficiencies: - It introduces latency, making it unsuitable for real-time applications. @@ -88,24 +207,121 @@ Post-hoc validation attempts to correct inaccuracies after generation by cross-r Factual Validation: Core Principles -Factual validation ensures that LLM outputs are verifiable and contextually accurate. This approach focuses on: +Factual validation ensures that LLM outputs are verifiable and contextually accurate. This approach is guided by two main principles: + +- **Granularity**: Responses are decomposed into discrete factual units for precise validation, reducing the likelihood of undetected inaccuracies . + + Mathematically, a generated response $$R$$ is represented as: + $$ + R = \{\textcolor{#1f77b4}{F_1}, \textcolor{#ff7f0e}{F_2}, \dots, \textcolor{#2ca02c}{F_n}\}, + $$ + where each $$F_i$$ represents a discrete factual unit, such as a statement or claim. + + #### Example: + + $$ + R = \{\text{"Paris is the capital of France"}, \text{"The Eiffel Tower is in Paris"}\}. + $$ -- **Granularity**: Decomposing responses into short factual units that allows for precise validation at the component level, reducing the likelihood of undetected inaccuracies . -- **Scalability**: By leveraging vectorized representations, validation systems can efficiently compare outputs against large, structured knowledge bases . + Validation is performed by comparing each $$F_i$$ against a knowledge base $$K$$, which contains validated information: + + $$ + K = \{\text{"Paris is the capital of France"}, \text{"The Eiffel Tower is in Paris"}\}. + $$ + + A factual unit $$F_i$$ is considered valid if it exists in $$K$$: + $$ + V(F_i) = + \begin{cases} + 1 & \text{if } F_i \in K, \\ + 0 & \text{otherwise.} + \end{cases} + $$ + + The overall validation score for the response is calculated as: + $$ + V(R) = \frac{\sum_{i=1}^{n} V(F_i)}{n}, + $$ + where $$V(R) \in [0, 1]$$ quantifies the proportion of factual units in $$R$$ that are verified by $$K$$. + +- **Scalability**: Validation systems leverage vectorized representations to efficiently compare outputs against large, structured knowledge bases . Instead of exact string matching, factual units $$F_i$$ are mapped into a high-dimensional vector space for efficient comparisons. The Role of Vectorization -Vectorization encodes atomic facts into high-dimensional embeddings, facilitating efficient storage, retrieval, and comparison . This method allows the system to: +Vectorization encodes atomic facts into high-dimensional representations (**embeddings**) that facilitate efficient storage, retrieval, and comparison . + +Each factual unit $$F_i$$ is transformed into an embedding: +$$ +\mathbf{v}_i = \textcolor{#1f77b4}{\text{Encode}(F_i)}, +$$ +where $$\text{Encode}(\cdot)$$ maps textual data to numerical vectors. + +#### Example: + +$$ +F_i = \text{"Paris is the capital of France"} +$$ +$$ +\mathbf{v}_i = [0.12, 0.45, 0.67, \dots, 0.89] +$$ + +The knowledge base $$K$$ is also represented as a set of embeddings: +$$ +K = \{\mathbf{k}_1, \mathbf{k}_2, \dots, \mathbf{k}_m\}. +$$ + +To validate $$F_i$$, the similarity between $$\mathbf{v}_i$$ and $$\mathbf{k}_j$$ is measured using cosine similarity: +$$ +\text{Sim}(\mathbf{v}_i, \mathbf{k}_j) = \frac{\mathbf{v}_i \cdot \mathbf{k}_j}{\|\mathbf{v}_i\| \|\mathbf{k}_j\|}. +$$ + +**Cosine Similarity**: Determines how closely aligned two vectors are in high-dimensional space (range: -1 to 1): +- $$1$$: Identical vectors. +- $$-1$$: Completely opposite vectors. + +A factual unit $$F_i$$ is considered valid if: +$$ +\max_{j} \text{Sim}(\mathbf{v}_i, \mathbf{k}_j) \geq \tau, +$$ +where $$\tau$$ is a predefined similarity threshold . This ensures that validation considers **semantic similarity** rather than exact matches. + +Combining Granularity and Vectorization + +Factual validation combines the precision of granularity with the efficiency of vectorization to determine the validity of responses. The process involves: + +1. **Decompose Response**: Break $$R$$ into: + $$ + R = \{\textcolor{#1f77b4}{F_1}, \textcolor{#ff7f0e}{F_2}, \dots, \textcolor{#2ca02c}{F_n}\}. + $$ + +2. **Encode Units**: Transform each $$F_i$$ into its high-dimensional embedding: + $$ + \mathbf{v}_i = \text{Encode}(F_i). + $$ + +3. **Compare with Knowledge Base**: Use cosine similarity to validate $$\mathbf{v}_i$$ against $$K$$: + $$ + \max_{j} \text{Sim}(\mathbf{v}_i, \mathbf{k}_j) \geq \tau. + $$ + +The overall validation score is computed as: +$$ +V(R) = \frac{\sum_{i=1}^{n} \mathbb{1} \left( \max_{j} \text{Sim}(\mathbf{v}_i, \mathbf{k}_j) \geq \tau \right)}{n}, +$$ +where $$\mathbb{1}(\cdot)$$ is the indicator function that outputs 1 if the condition is satisfied and 0 otherwise. -- Quickly identify semantically relevant information from knowledge bases . -- Enable context-aware validation that adapts to the specific demands of a query . +**This approach ensures**: +1. **Precision** at the **component level** (granularity). +2. **Scalability** across **large knowledge bases** (vectorization) . ## Our Proposal: Fact-Based Validation for GPT Systems To address the challenges of ensuring factual accuracy in GPT systems, we propose a framework that preprocesses data into structured and verifiable units with the aim to enhance the reliability of LLM-generated responses while optimizing storage and retrieval efficiency.
- -
This diagram illustrates the workflow of the LLM validity judger. The process involves simplifying the LLM response into facts, fetching nearby facts from a vector database, and assessing the validity of the response based on this context. It then generates an overall validity score with an explanation. The diagram also illustrates a separate system called “New Data Input System” for inputting new data into the vector database, which operates independently from the validity judgment process. The fetching system depicted is the same as existing vector database fetching systems, simply shown for clarity.
+ +
+ Figure 5. This diagram illustrates the workflow of the LLM Validity Judger. The system fetches relevant facts from a vector database, generates responses based on this context, and simplifies outputs into factual statements. It validates these statements by comparing them with the vector database, generating an overall validity score and explanation. The process repeats with feedback until a valid result is achieved. The diagram also includes the New Data Input System, which independently adds summarized and verified facts into the vector database, ensuring its relevance and accuracy. For clarity, the fetching system mirrors standard vector database retrieval processes. +
System Architecture diff --git a/assets/bibliography/2025-04-28-streamlining-factual-validation.bib b/assets/bibliography/2025-04-28-streamlining-factual-validation.bib index 5cd59742..9f42b75c 100644 --- a/assets/bibliography/2025-04-28-streamlining-factual-validation.bib +++ b/assets/bibliography/2025-04-28-streamlining-factual-validation.bib @@ -91,6 +91,17 @@ @article{wei2022 year={2022} } +@journal{bishop1992, + title={Exact Calculation of the Hessian Matrix for the Multilayer Perceptron}, + author={Bishop, Christopher M.}, + journal={Neural Computation}, + volume={4}, + number={4}, + pages={494--501}, + publisher={MIT Press}, + year={1992} +} + @article{howard2018, title={Universal Language Model Fine-tuning for Text Classification}, author={Howard, Jeremy and Ruder, Sebastian}, diff --git a/assets/img/2025-04-28-streamlining-factual-validation/Method.png b/assets/img/2025-04-28-streamlining-factual-validation/Method.png index 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