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# Introduction
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Source code for the ICLR 2024 paper: Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models; Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones, Suriya Gunasekar, Ranjita Naik, Hamid Palangi, Ece Kamar, Besmira Nushi.
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# Getting Started
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The repository consists of:
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- Scripts for collecting attention-based metrics from open source models from the Llama-2 family at inference time.
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- Scripts for using these metrics across a given dataset and training a simple linear probe that can predict whether the model will make a factual error.
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- A simplistic tool for visualizing model attention on a given instance.
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- Relevant factual knowledge data that was used in the paper for evaluation
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**Installation**:<br>
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The code is written in Python 3.11, and you can use `requirements.txt` to install the required packages.
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```bash
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```
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And here is the visualization:
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![attention_flow3](./assets/sample_mc.png)
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### Detecting Factual Errors
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Our probing experiments have 2 main steps:
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- Collect attention-based metrics for a given dataset. This is done using the `main_flow_collection.py`.

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