GPT-Bargaining: Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
Implmentation of paper: Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback. [Arxiv] 2023.
Yao Fu. University of Edinburgh
We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We ask two large language models to negotiate with each other, playing the roles of a buyer and a seller, respectively. They aim to reach a deal with the buyer targeting a lower price and the seller a higher one. A third language model, playing the critic, provides feedback to a player to improve the player’s negotiation strategies. We let the two agents play multiple rounds, using previous negotiation history and the AI feedback as in-context demonstrations to improve the model’s negotiation performance iteratively.
We believe our results have profound implications for AI research: on the positive side, it indicates the possibility of continuously improving language models with minimal human intervention. On the risky side, it might be more challenging to oversight the model behavior in our framework because models are acting autonomously, which calls for future alignment and safety research in the multi-agent game setting.
mkdir outputs
api_key=<YOUR_OPENAI_API_KEY>
anthropic_api_key=<YOUR_ANTHROPIC_API_KEY>
game_type=criticize_seller
moderator_instruction=moderator_0509
verbose=1
n_round=10
n_rollout=5
n_exp=200
ver=criticize_claude_instant_seller
seller_engine=claude-instant-v1.0
seller_critic_engine=claude-instant-v1.0
game_version=${game_type}_${n_exp}_runs_${n_rollout}_rollout_ver_${ver}
python run.py\
--api_key=${api_key}\
--anthropic_api_key=${anthropic_api_key}\
--seller_engine=${seller_engine}\
--seller_critic_engine=${seller_critic_engine}\
--game_type=${game_type}\
--verbose=${verbose}\
--n_round=${n_round}\
--n_exp=${n_exp}\
--n_rollout=${n_rollout}\
--moderator_instruction=${moderator_instruction}\
--ver=${ver}\
--game_version=${game_version} Code structure:
agent.py: implementation of different agents
lib_api.py: wrappers of LLM APIs
run.py: run the bargaining game!
lib_prompt: prompt library used in this project
exps: experiments run in this project
notebooks: visualization tools
- Include chat-bison-001
- Finish claude-100k