This project focuses on fine-tuning large language models (LLMs) like GPT-3.5-turbo and Llama-3-8B on Google's Synthetic Persona Chat dataset to enhance persona consistency in chatbots.
The main objective of this project is to create chatbots that maintain a consistent persona throughout conversations. The models were fine-tuned using Google's Synthetic Persona Chat dataset and evaluated using various metrics such as BLEU, ROUGE, BERTScore, and perplexity. The fine-tuned models demonstrated significant improvements in persona coherence and overall performance compared to baseline models.
- Fine-tuning of LLMs: Utilized GPT-3.5-turbo and Llama-3-8B for fine-tuning on persona chat datasets.
- Evaluation Metrics: Models were assessed using BLEU, ROUGE, BERTScore, and perplexity metrics.
- Enhanced Persona Consistency: Achieved improved persona coherence in chatbot responses.
- Quantitative and Qualitative Analysis: Both automated metrics and human evaluations were used to assess model performance.
We used Google's Synthetic Persona Chat dataset, which provides high-quality conversational data with predefined personas. This dataset ensures that the generated conversations are faithful to the user personas.
- Data Preparation: Converted the training data into JSONL format suitable for model fine-tuning.
- Prompt Engineering: Developed effective prompts for persona injection.
- Model Training: Fine-tuned GPT-3.5-turbo and Llama-3-8B on the prepared dataset.
The models were evaluated using the following metrics:
- BLEU: Measures the similarity between generated text and reference translations.
- ROUGE: Assesses the overlap between generated and reference texts.
- BERTScore: Evaluates semantic similarity using contextual embeddings.
- Perplexity: Measures the quality of the generated text.
The fine-tuned models outperformed traditional models in both quantitative metrics and human evaluations. The results highlighted the effectiveness of our fine-tuning approach in enhancing persona coherence in chatbots.
- Cost of Fine-tuning: Fine-tuning on OpenAI's GPT-3.5-turbo was expensive, suggesting a need for more budget-friendly solutions.
- Prompt Engineering: Further research is needed to optimize prompt engineering techniques.
- Evaluation Metrics: Current metrics do not fully align with human judgment, indicating a need for better automated evaluation methods.
- Personalizing Dialogue Agents: I have a dog, do you have pets too?
- Faithful Persona-based Conversational Dataset Generation with Large Language Models
- ParlAI: A Framework for Training and Evaluating AI Models on a Variety of Conversational Tasks
- Harshil Bhullar - [email protected]
- Siddarth Chalasani - [email protected]
- Krish Patel - [email protected]