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readme.md

Bayesian Inference with Large Language Models Using BQA and COT Methods

Important Update

Note: The gpt-3.5-turbo-0613 model is deprecated and replaced with gpt-3.5-turbo. Consequently, results obtained using gpt-3.5-turbo may differ from those reported in earlier experiments using gpt-3.5-turbo-0613.

Installation

  1. Clone the repository:
git clone https://github.com/HLR/BLInD.git
cd BLInD
  1. Install the required dependencies:
python -m pip install --upgrade pip
pip install -r requirements.txt
cd BQAandCOT

Usage

To query LLMs for Bayesian inference using BQA and COT methods, use the main.py script:

python main.py [--testdataset TESTDATASET] [--outputdataset OUTPUTDATASET] [--openaikey OPENAIKEY]
[--openaiorg OPENAIORG] [--replicatekey REPLICATEKEY] [--method {BQA,COT}] [--samplenum SAMPLENUM]
[--models MODEL [MODEL ...]] [--maxattempt MAXATTEMPT] [--CLADDER]

Arguments

  • --testdataset: Input test dataset (default: "../datasets/Colored_1000_examples.csv")
  • --outputdataset: Dataset folder to save the results (default: "../datasets/")
  • --openaikey: OpenAI API key
  • --openaiorg: OpenAI organization key
  • --replicatekey: Replicate.ai API key (required for non-GPT models)
  • --method: Method to solve the problem (choices: "BQA", "COT", default: "BQA")
  • --samplenum: Number of instances of the dataset to read (default: 2000)
  • --models: Choose one or more models (choices: "gpt-3.5-turbo", "gpt-4-0613", "meta/meta-llama-3-70b-instruct", "mistralai/mistral-7b-instruct-v0.2", "meta/llama-2-70b-chat")
  • --maxattempt: Max number of attempts after a failed prompt to OpenAI (default: 10)
  • --CLADDER: Use CLADDER dataset (default: False)

Note: For non-GPT models (Llama, Mistral), you need to provide a Replicate.ai API key using the --replicatekey argument.

This program saves every answer after each prompt. If it terminates, run it again, and it will pick up where it left off.

Testing LLMs for Bayesian Inference

To test LLMs using BQA and COT, use the test.py script:

python test.py [--testdataset TESTDATASET] [--outputdataset OUTPUTDATASET]
[--models MODEL [MODEL ...]] [--CLADDER]
  • --testdataset: Input test dataset (default: "../datasets/Colored_1000_examples.csv")
  • --outputdataset: Dataset folder that has saved the results (default: "../datasets/")
  • --models: Choose one or more models (same choices as main.py)
  • --CLADDER: Use CLADDER dataset (default: False)

Dataset

The code uses a test dataset specified by the --testdataset argument. By default, it uses the "../datasets/Colored_1000_examples.csv" dataset. If the --CLADDER flag is set, it uses the "../datasets/CLADDER_test.csv" dataset instead.

Models

The code supports running Bayesian inference with the following LLMs:

  • GPT-3.5-turbo
  • GPT-4-0613
  • Meta Llama 3 70B Instruct
  • Mistral 7B Instruct v0.2
  • Llama 2 70B Chat

You can specify one or more models to use with the --models argument.

Output

The results of running Bayesian inference are saved in the dataset folder specified by the --outputdataset argument. The output files are named based on the arguments set in main.py.