DebUnc: Mitigating Hallucinations in Large Language Model Agent Communication with Uncertainty Estimations
This repo contains the code and data for the paper "DebUnc: Mitigating Hallucinations in Large Language Model Agent Communication with Uncertainty Estimations".
git clone https://github.com/lukeyoffe/debunc.git
cd debunc
conda create --name debunc python=3.10 -y
conda activate debunc
pip install -e .
To use restricted models, log in to Hugging Face with the following command:
huggingface-cli login
The scripts to run and evaluate on various benchmarks can be found in src/debate/.
To use Llama 3 instead of Mistral 7B, replace "mistralai/Mistral-7B-Instruct-v0.2" with "meta-llama/Meta-Llama-3-8B-Instruct". Other models are not currently supported.
To use TokenSAR instead of Mean Token Entropy, replace ue_method = MeanTokenEntropy() with ue_method = TokenSAR()
src/models/demo.ipynb contains a demonstration of attention scaling applied to RAG.
@article{yoffe2024debunc,
title={DebUnc: Mitigating Hallucinations in Large Language Model Agent Communication with Uncertainty Estimations},
author={Yoffe, Luke and Amayuelas, Alfonso and Wang, William Yang},
journal={arXiv preprint arXiv:2407.06426},
year={2024}
}
The code in src/lm-polygraph is based on the LM-Polygraph project, and contains implementations for various uncertainty metrics.
The modeling_*.py files in src/models are based on the Huggingface Transformers library, with modifications to perform attention scaling. The attention scaling occurs between ##### <AttentionScaling> ##### and ##### </AttentionScaling> #####.