DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucination

Overview of the DeCoRe workflow

Paper: https://arxiv.org/abs/2410.18860

Authors: Aryo Pradipta Gema$^{Q,K}$, Chen Jin$^{K}$, Ahmed Abdulaal$^{K,V}$, Tom Diethe$^{K}$, Philip Teare$^{K}$, Beatrice Alex$^{Q}$, Pasquale Minervini$^{Q}$, Amrutha Saseendran$^{K}$

$^{Q}$ University of Edinburgh, United Kingdom

$^{K}$ Centre for AI, Data Science \& Artificial Intelligence, R\&D, AstraZeneca, United Kingdom

$^{V}$ University College London, United Kingdom

🛠️ Setup

🌏 Environment variable

Setup an .env file in the root folder

nano .env

HF_TOKEN=<your_huggingface_write_access_token>

📦 Required Packages

🐍 conda

conda env create -f environment.yaml
conda activate decore

🐍 pip

pip install -r requirements.txt

For development, we use black and isort. If you wish to proceed without them and if you are using VSCode, update .vscode/settings.json accordingly.

🍄 Retrieval Heads

The retrieval heads for the models can be found in the retrieval_heads folder.

To reproduce these (or if you just want to experiment with newer models or try this step yourself), visit the Retrieval_Head repository to detect the retrieval heads for each model. For instance:

# Llama3-8B-Instruct
python retrieval_head_detection.py  --model_path meta-llama/Meta-Llama-3-8B-Instruct --s 0 --e 5000

# Llama3-70B-Instruct
python retrieval_head_detection.py  --model_path meta-llama/Meta-Llama-3-70B-Instruct --s 0 --e 5000

# Mistral-7B-v0.3-Instruct
python retrieval_head_detection.py  --model_path mistralai/Mistral-7B-Instruct-v0.3 --s 0 --e 5000

# Qwen2-7B-Instruct
python retrieval_head_detection.py  --model_path Qwen/Qwen2-7B-Instruct --s 0 --e 5000

💡 TIP: If you fail to do the retrieval head detection for Qwen2 in a multi-gpu setup, try using only 1 GPU (e.g., CUDA_VISIBLE_DEVICES=0).

🪄 To WandB or not to WandB

If you wish to use WandB, please update the configs/config.yaml, specifically the values of wandb_project and wandb_entity. We generally recommend using WandB, but if you prefer not to, you can still run the script using the debug flag or by setting the value of debug in configs/config.yaml into true. This will bypass the wandb initialisation and logging.

🌲 Directory Structure

.
├── README.md
├── environment.yaml
├── requirements.txt
├── .env.example                     # Example environment file
├── .env                             # Your environment file
├── configs/                         # Hydra configs
│   ├── config.yaml                  # Default config values that will be replaced by experiment config
│   ├── data/                        # Directory containing dataset config files, that will be used in the experiment config files
│   ├── data_loader/                 # Directory containing one default data loader config file
│   ├── decoder/                     # Directory containing decoder config files (e.g., DeCoRe, Baseline, DoLa, ITI), that will be used in the experiment config files
│   ├── experiment/                  # Directory containing experiment config files per decoder
│   └── model/                       # Directory containing model config files, that will be used in the experiment config files
├── data/                            # Directory containing dataset files
├── docs/                            # Directory containing assets for documentation
├── notebooks/                       # Jupyter notebooks directory, only for creating plots
├── retrieval_heads/                 # Directory containing pre-computed retrieval heads
├── scripts/
│   ├── main.py                      # The main script for evaluating the runs
└── src/
    ├── __init__.py
    ├── configs.py                   # Handle Hydra configs
    ├── datasets/                    # Dataset classes
    ├── factories.py                 # Factory functions to help with instantiating dataset, model, and metric classes. Called in the run.py
    ├── metrics/                     # Metrics classes (the name must match the dataset classes)
    ├── models/                      # Model classes, instantiating the selected models and decoder method
    ├── run.py                       # The run manager, handling the selection of dataset, model, and metric classes, initializing WandB, etc.
    └── utils/
        ├── __init__.py
        ├── common_utils.py          # Common utility functions
        ├── modelling_llama.py       # Minimally modified from the Retrieval head repository
        ├── modelling_mistral.py     # Minimally modified from the Retrieval head repository
        └── modelling_qwen2.py       # Minimally modified from the Retrieval head repository

📝 Evaluation

General evaluation

The evaluation uses the scripts/main.py and a Hydra config file from the configs/experiment folder. For instance:

python scripts/main.py experiment=memotrap/decore_entropy/llama3_8b_instruct decoder.configs.num_retrieval_heads=100

Notice that the hydra configs are structured based on dataset_name > decoder_name > model_name.yaml. It is then followed with custom hyperparameters (e.g., decoder.configs.num_retrieval_heads).

Some dataset can be further modified depending on the variation that you want to evaluate the model against. The options are:

NQ: closed_book, oracle
MuSiQue: direct_closed_book, direct_open_book, cot_closed_book, cot_open_book

Therefore, to run NQ with closed_book setting, you can simply do:

python scripts/main.py experiment=nq/decore_entropy/llama3_8b_instruct decoder.configs.num_retrieval_heads=10 data.variation=closed_book

Please check examples of how to design the bash commands in evaluation_bash_commands_example.md which contain the examples on how to run evaluations of $\text{DeCoRe}_{\text{entropy}}$ with Llama3-8B-Instruct on multiple datasets.

TruthfulQA Gen Evaluation

Add OpenAI API key to your .env file:

OPENAI_API_KEY=<your_openai_api_key>

Fine tune davinci-002 using the data that can be found in data/TruthfulQA_eval_fine_tune

Set the fine-tuned model id to the .env file

GPT_JUDGE_NAME=<your_gpt_judge_fine_tuned_model_id>
GPT_INFO_NAME=<your_gpt_info_fine_tuned_model_id>

The ids of both fine-tuned models would usually be prefixed by ft:davinci-002:....

Download the predictions from WandB (if you follow this codebase, it will be in a json format). Amd pass it on to the evaluation script.

# Evaluate!

python src/metrics/truthfulqa_gen.py --pred_filepath=path/to/truthfulqa_model_prediction.json

🙏 Citation

@article{gema2024decore,
      title={DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations}, 
      author={Aryo Pradipta Gema and Chen Jin and Ahmed Abdulaal and Tom Diethe and Philip Teare and Beatrice Alex and Pasquale Minervini and Amrutha Saseendran},
      year={2024},
      eprint={2410.18860},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.18860}, 
}

aryopg / decore

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