tqtensor / cot-selfevolve

CoT-SelfEvolve Framework

CoT-SelfEvolve

Reproduce the Experiments

Presequites

AWS CLI

You need to install AWS CLI and have an AWS account to run DVC to pull data from S3. You can install AWS CLI by following the instructions here.

Python dependencies

Due to the constraints of DS-1000, they expect to run the benchmark tests on specific versions of the libraries, so we have to maintain the Python dependencies at a specific version of Python 3.8. To install the necessary dependencies, run the following command:

pip install -U poetry
poetry install

LLMs

Currently, we support running with the following LLMs:

• Azure OpenAI
• OpenAI
• Vertex AI
• AWS Bedrock

Depending on which LLM you have access to, please fill in the .env file with the necessary credentials.

AZURE_API_KEY=
AZURE_API_BASE=
AZURE_API_VERSION=

AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION_NAME=

OPENAI_API_KEY=

Especially for Vertex AI, you need to put the service account key in the src/vertex_key.json file.

Start the Vector DB

To start the Vector DB, you can run the following command to download the embeddings and start the Chroma server:

poetry run dvc pull
bash scripts/docker_compose.sh

Running the Experiments

You can control the experiment settings through command-line arguments when running the main.py file. Here are the available options:

• --experiment_name: The name of the experiment.
• --sampling_fraction: The fraction of the dataset to sample.
• --initial_strategy: The initial strategy to use, either Chain-of-Thought (COT) or Zero-Shot (ZEROSHOT).
• --correction_strategy: The correction strategy to use, either Chain-of-Thought (COT) or Zero-Shot (ZEROSHOT).
• --initial_model: The initial LLM model to use at the initial stage.
• --correction_model: The correction LLM model to use at the correction stage.
• --temperature: The temperature setting for LLM.
• --top_p: The top-p setting for LLM.
• --self_correction: Enable self correction. Use --no-self_correction to disable.
• --max_self_correction_attempts: Max self correction attempts.
• --demo: Run in demo mode with reduced logging. Use --no-demo to disable.

To run the experiment with custom settings, execute the following command in your terminal:

python main.py --experiment_name <experiment_name> \
    --sampling_fraction <fraction> \
    --initial_strategy <strategy> --correction_strategy <strategy> \
    --initial_model <model_name> \
    --correction_model <model_name> \
    --temperature <value> --top_p <value> \
    --self_correction --max_self_correction_attempts <attempts> --demo

Acknowledgements

I would like to express my gratitude to the following individuals for their valuable contributions to this project:

• Sean from PixelML deserves special recognition for his substantial support in providing LLMs credits.