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MastermindEval

Evaluating Reasoning Capabilities of LLMs Using the Mastermind Board Game.

Game Overview

Installation | Evaluation Paradigms | Basic Concepts | Running the Evaluations | Citation

🚀 Installation

To set up the environment and install dependencies, run the following:

conda create -n mastermind python=3.11
conda activate mastermind
pip install -e .

🏆 Evaluation Paradigms

We provide three different evaluation paradigms:

  1. 🤖 Agentic Evaluation: The model actively plays Mastermind, interacting with the game environment.
  2. 📝 Prompt-Based Evaluation: The model is presented with pre-played game scenarios and must deduce the last possible code.
  3. 🎯 Multiple-Choice Evaluation: The model ranks different code options based on log-likelihood, aligning with pretraining objectives. We will integrate this option into the lm-eval-harness library soon.

🔑 Basic Concepts

  • 🧩 Model Class: Defines the LLM interface that interacts with the game. We provide support for:
    • Hugging Face Model Hub
    • OpenAI
    • Anthropic
  • 🎲 Mastermind Game Class: Represents a game instance with customizable parameters such as num_colors and possible_colors.
  • 📊 Evaluator Class: Manages the evaluation process by executing multiple rounds of the game and assessing model performance.

Running the Evaluations

We provide various scripts for running different evaluation methods:

  • 🤖 Agentic Evaluation: run_full_game.py (Python script) and run_full_game.sh (Bash script)
  • 📝 Prompt-Based Evaluation: run_instructions.py (Python script) and run_instructions.sh (Bash script) - These splits are also availabe on the 🤗 Hugging Face hub!
  • 🎯 Multiple-Choice Evaluation: run_multiple_choice.sh (Bash) – relies on lm-eval-harness (pending) - These splits are also availabe on the 🤗 Hugging Face hub!

Example Usage

Below is a conceptual overview of running an evaluation using a Hugging Face model:

from mastermind.evaluator import Evaluator
from mastermind.game import Mastermind
from mastermind.models import HFModel
from mastermind.utils import print_summary

# Load the model
model = HFModel(model_name='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B')

# Initialize the game environment
game = Mastermind(code_length=4, num_colors=6)

# Create the evaluator
evaluator = Evaluator(game, model, use_cot=True, use_fewshot_example=True)

# Run the evaluation
result = evaluator.run(num_games=100, save_results=True, save_path="results", compute_progress=True)

# Display summary
print_summary(model, game, result, num_runs=100)

📚 Citation

Coming soon.

For any issues, feel free to open an issue or contribute to the repository! 🚀