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Bayesian Inference with Large Language Models Using ProbLog Method

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 Problog

API Keys Setup

This project requires API keys for different LLM providers:

  1. For OpenAI models (gpt-3.5-turbo, gpt-4-0613), you'll need:

    • OpenAI API key
    • OpenAI organization key

  2. For other models (Llama, Mistral), you'll need:

    • Replicate.ai API key

The Replicate.ai API key is required to access models like meta/llama-2-70b-chat, meta/meta-llama-3-70b-instruct, and mistralai/mistral-7b-instruct-v0.2.

Usage

To query LLMs for Bayesian inference using ProbLog method, use the main.py script:

python main.py [--testdataset TESTDATASET] [--outputdataset OUTPUTDATASET] 
[--openaikey OPENAIKEY] [--openaiorg OPENAIORG] [--replicatekey REPLICATEKEY]
[--samplenum SAMPLENUM] [--models MODELS [MODELS ...]] [--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 for non-OpenAI models
  • --samplenum: Number of instances of the dataset to read (default: 2000)
  • --models: Choose one or more models from:
    • 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 (default: 10)
  • --CLADDER: Use CLADDER dataset (default: False)

This program saves every answer after each prompt. If it terminates, run it again, and it will pick up where it left off. The program by default tests each method with and without NE and GG and saves their results separately.

Testing LLMs for Bayesian Inference

To test LLMs using ProbLog method, use the test.py script:

python test.py [--testdataset TESTDATASET] [--outputdataset OUTPUTDATASET]
[--models MODELS [MODELS ...]] [--CLADDER]

Test Script Arguments

  • --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 various LLMs:

  • OpenAI Models:
    • gpt-3.5-turbo
    • gpt-4-0613
  • Replicate.ai Models:
    • meta/meta-llama-3-70b-instruct
    • mistralai/mistral-7b-instruct-v0.2
    • meta/llama-2-70b-chat

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.