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This repo contains the source code for reproducing the experimental results in semantic density paper (Neurips 2024)

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semantic-density-paper

This repo contains the source code for reproducing the experimental results reported in paper: "Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space", which is accepted to Neurips 2024 (Arxiv link: https://arxiv.org/abs/2405.13845).

Environment Setup

Below are the step-by-step guideline for setting up the experiment environment:

(a) Use the environment_llama2_mistral_mixtral.yml file to create an anaconda environment for all the experiments with Llama-2-13B, Llama-2-70B, Mistral-7B, Mixtral-8x7B and Mixtral-8x22B.

(b) Replace anaconda3/envs/{your_env_name_for_llama2_mistral_mixtral}/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py with modeling_llama2.py in folder huggingface_replacement (change the file name to modeling_llama.py).

(c) Replace anaconda3/envs/{your_env_name_for_llama2_mistral_mixtral}/lib/python3.10/site-packages/transformers/models/mistral/modeling_mistral.py with modeling_mistral.py in folder huggingface_replacement.

(d) Replace anaconda3/envs/{your_env_name_for_llama2_mistral_mixtral}/lib/python3.10/site-packages/transformers/models/mixtral/modeling_mixtral.py with modeling_mixtral.py in folder huggingface_replacement.

(e) Use the environment_llama3.yml file to create an anaconda environment for all the experiments with Llama-3-8B and Llama-3-70B.

(f) Replace anaconda3/envs/{your_env_name_for_llama3}/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py with modeling_llama3.py attached (change the file name to modeling_llama.py).

Running Experiments

Please read the detailed step-by-step guideline inside folder experiment_code to generate the experimental results.

Citation

If you find semantic density useful, please cite it using the following BibTeX entry:

@inproceedings{qiu2024semantic,
title={Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space},
author={Qiu, Xin and Miikkulainen, Risto},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
}

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This repo contains the source code for reproducing the experimental results in semantic density paper (Neurips 2024)

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