Awesome

LLM Debate

Overview

Code release for our paper Debating with More Persuasive LLMs Leads to More Truthful Answers

Setup

Prerequisites

• Python 3.11
• Virtual environment tool (e.g., virtualenv)

Installation

1. Create and Activate a Virtual Environment:

   virtualenv --python python3.11 .venv
   source .venv/bin/activate
   

2. Install Required Packages:

   pip install -r requirements.txt
   

3. Install Pre-Commit Hooks:

   make hooks
   

4. Create a SECRETS file

   API_KEY=<KEY>
   ANTHROPIC_API_KEY=<KEY>
   DEFAULT_ORG=
   

5. If you want to use the frontend also add the following

   DB_USER=psqluser
   DB_PASSWORD=password
   DB_HOST=localhost
   DB_PORT=5432
   DB_NAME=debate
   

Usage

Minimal Script

We recommend our minimal script to run everything end-to-end: ./scripts/reproduce_minimal.sh. This will run all protocols (blind, consultancy, debate, interactive debate, expert judge) with GPT-4-Turbo expert and non-experts on 10 questions from QuALITY. Then run notebook scripts/plot_minimal.ipynb to generate a minimal plot similar to Figure 1 in the paper. Running ./scripts/run_figure1.sh will reproduce the LLM results in Figure 1 but uses best-of-16 so watch your OpenAI usage limit.

Running Custom Experiments

# Run a debate and judgement on one question from the train-set of QuALITY
python -m core.main exp_dir='./exp/test1' +experiment='debate' +index=0 +swap=False

# Use a Claude 2.1 debater using best-of-8 and only 2 rounds of debate
python3 -m core.main exp_dir='./exp/test2' +experiment='debate' +index=0 +swap=False \
    ++correct_debater.BoN=8 \
    ++incorrect_debater.BoN=8 \
    ++correct_debater.language_model.model='claude-2.1' \
    ++incorrect_debater.language_model.model='claude-2.1' \
    ++rollout.num_steps=2 \
    ++anthropic_num_threads=10

# Debate with GPT-4-Turbo models and best-of-4 followed by judging and scoring on the train split of QuALITY
python -m core.debate \
    exp_dir='./exp/test3' \
    +experiment='debate'\
    ++correct_debater.language_model.model='gpt-4-1106-preview' \
    ++incorrect_debater.language_model.model='gpt-4-1106-preview' \
    ++correct_debater.BoN=4 \
    ++incorrect_debater.BoN=4 \
    ++max_num_from_same_story=5 \
    ++split=train
python -m core.judge \
    exp_dir='./exp/test3' \
    +experiment='debate'\
    ++judge.language_model.model='gpt-4-1106-preview' \
    ++judge_name='gpt-4-turbo'
python -m core.scoring.accuracy \
    exp_dir='./exp/test3' \
    +experiment='debate'\
    ++judge_name='gpt-4-turbo'

# Correct Consultantacy
python -m core.debate  \
    exp_dir='./exp/test3' \
    +experiment='consultancy' \
    method_type='correct'

# Incorrect Consultantacy
python -m core.debate  \
    exp_dir='./exp/test3' \
    +experiment='consultancy' \
    method_type='incorrect'

We can also call methods within other python scripts:

from core.debate import main as debate
from hydra import compose, initialize

with initialize(version_base=None,
    config_path="../core/config/quality",
    job_name="test4"):

    cfg = compose(
        config_name="config",
        overrides=["method=debate","exp_dir=./exp/test4"])
    debate(cfg)

Datasplits

• T_h: Testset used in the human trial with 47 questions. Use options: ++max_num_from_same_story=5 ++split=both ++human_experiments=8
• T_l: Testset used by LLM experiments with 400 questions. Use options ++max_num_from_same_story=5 ++split=train
• D_l: Development set used by LLM experiments with 291 questions. Use options ++max_num_from_same_story=5 ++split=dev

Data format

Each csv requires has following fields. This csv file is generated by load/quality.py and it is then populated with debate transcripts by debate.py and judge answers by judge.py.

Web Interface

Running the frontend

cd web/frontend && npm install && npm run dev

Running the backend

uvicorn web.backend.main:app --reload

Repository Structure

• core/main.py: Script for testing debate and consultancy protocols on one question.
• core/debate.py: Script for running debate protocol on a set of questions.
• core/judge.py: Script for running a judge on the output transcripts of debate.py.
• core/scoring/accuracy.py: Script for computing accuracy of judge answers.
• core/tournament.py: Script for running a cross-play tournament with many debaters.
• core/scoring/rating.py: Script for computing aggregate Elo rating debaters.
• core/llm_api: Directory containing modules for async LLM inference
• scripts: Contains the scripts to reproduce the main figures in the paper and otehr minimal examples.
• scripts/human_trial_example: Contains the script and config to reproduce a human experiment.
• data: zip files containing LLM debates from the human trial
• web: Contains the code for the web frontend and backend to host a human trial or view debate transcripts.

Contributing

Contributions to this repository are welcome. Please follow the standard procedures for submitting issues and pull requests.

Citation

If you would like to cite our work or data, you may use this citation:

@misc{khan2024debating,
      title={Debating with More Persuasive LLMs Leads to More Truthful Answers}, 
      author={Akbir Khan and John Hughes and Dan Valentine and Laura Ruis and Kshitij Sachan and Ansh Radhakrishnan and Edward Grefenstette and Samuel R. Bowman and Tim Rocktäschel and Ethan Perez},
      year={2024},
      eprint={2402.06782},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}