GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations

• Authors: Jinhao Duan*, Renming Zhang*, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, and Kaidi Xu (*equal contribution)
• arXiv
• GTBench HF Leaderboard

Overview

This repo contains code for our paper: GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations. GTBench is a language-driven environment, evaluating the strategic reasoning limitations of LLMs through game-theoretic tasks. GTBench is built on top of OpenSpiel, supporting 10 widely-recognized games:

• Tic-Tac-Toe
• Connect-4
• Breakthrough
• Nim
• Blind Auction
• Kuhn Poker
• Liar's Dice
• Negotiation
• Pig
• Iterated Prisoner's Dilemma

Environment

Dependencies can be installed by running

pip install -r requirements.txt

LLM Inference

GTBench uses LangChain for LLM Inference (./gamingbench/chat/chat.py), supporting

• Remote API access such as OpenAI / Anyscale / DeepInfra.
• OpenAI-Compatible APIs via FastChat.

Scripts

LLM-vs-X

GTBench supports

• LLM-vs-Conventional: LLM agent competes against conventional solvers such as Monte Carlo Tree Search (MCTS)
• LLM-vs-LLM: LLM agent competes against other LLM agents.

The following is a script for GPT-3.5-turbo-1106 w/ Prompt Agent vs. GPT-3.5-turbo-1106 w/ CoT Agent, over Tic-Tac-Toe

seed=0
output_root="./experiments"
exp_name='test'
num_matches=50 # number of matches
num_workers=20 # run 20 matches in parallel
threshold_matches=100 # maximum number of matches, stop criteria for low completion rate, e.g., LLM agents always generate illegal actions.
# suports all the games listed in ./gamingbench/configs/game_configs/*.yaml
game_name='tictactoe'
# supports all the llms defined in ./gamingbench/configs/model_configs/*.yaml
model_config_root='gamingbench/configs/model_configs'
llm_name='gpt-35-turbo-1106'
opponent_llm_name='gpt-35-turbo-1106'
# supports all the reasoning methods defined in ./gamingbench/agent_configs/*.yaml
agent_config_root='gamingbench/configs/agent_configs'
agent_name='prompt_agent'
opponent_agent_name='cot_agent'
declare -a api_keys=("<YOUR-OPENAIAPI-KEY>" "<YOUR_DEEPINFRA_KEY>")

python3 -m gamingbench.main \
    --num-matches ${num_matches} \
    --exp-root ${output_root}/${exp_name}/${llm_name} \
    --seed ${seed} \
    --game-name ${game_name} \
    --agent-configs ${agent_config_root}/${agent_name}.yaml ${agent_config_root}/${opponent_agent_name}.yaml \
    --model-configs ${model_config_root}/${llm_name}.yaml ${model_config_root}/${opponent_llm_name}.yaml \
    --api-keys ${api_keys[@]} \
    --exchange-first-player \
    --num-workers ${num_workers} \
    --threshold-matches ${threshold_matches}

Customized LLM Agent

Will be ready soon.

Upload to GTBench HF Leaderboard

Will be ready soon.

Reference

Please cite our paper as

@article{duan2024gtbench,
  title={GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations},
  author={Duan, Jinhao and Zhang, Renming and Diffenderfer, James and Kailkhura, Bhavya and Sun, Lichao and Stengel-Eskin, Elias and Bansal, Mohit and Chen, Tianlong and Xu, Kaidi},
  journal={arXiv preprint arXiv:2402.12348},
  year={2024}
}