Evaluating Language Model Agency through Negotiations

This repository contains the official implementation for the ICLR paper Evaluating Language Model Agency through Negotiations [1].

TL;DR: let language models negotiate to find out how well they function as autonomous agents. (🤖💬🤝🤖💬)

Interested in learning more?

• 🎓 We wrote a paper
• 🗂️ Shared our data
• 📝 And wrote a blogpost

Overview

Language Models (LMs) are increasingly being used to power 'agents', capable of planning and interacting over multiple steps. Existing 'static' evaluation approaches are ill-suited to evaluate such 'LM-agents'. Specifically, they do not:

1. 🔄 Measure behavior over extended periods
2. 🤖💬🤖 Allow for cross-model interaction
3. 🤝⚖️🏆 Jointly evaluate alignment and performance metrics

Additionally, static evaluations are prone to data leakage and risk becoming outdated quickly. Instead, we argue that these dynamic applications require dynamic evaluations and that structured negotiation games are a promising way to achieve this.

What can you find in this repository?

• [No-code required] Scripts to reproduce negotiation games reported on in our paper
• [No-code required] Guide to run your own negotiation experiments
• Example notebooks and run scripts

Getting Started!

The simplest way to get started is to:

1. clone this repository, then
2. create a secrets.json file in the root directory looking as follows:

{
    "openai": {
        "api_key": "<your-key>"
    },
    "azure": {
        "api_key": "<your-key>"
    },
    "anthropic": {
        "api_key": "<your-key>"
    },
    "google": {
        "api_key": "<your-key>"
    },
    "cohere": {
        "api_key": "<your-key>"
    }
}

In this file, insert your own API key for one of the following providers: {Anthropic, Cohere, Google, OpenAI, Microsoft}. This secrets.json file is part of the .gitignore, to prevent you from accidentally pushing your raw keys to GitHub :). (see 'note' below if using Google/Azure models)

Next, create a virtual environment and install the packages listed in requirements.txt. Once this is done you're all set! Navigate to this project's root directory, update the <your_model_name>, <your_model_provider> and run:

# model_name: one of {gpt-3.5-turbo, gpt-4, claude-2.0, claude-2.1, chat-bison, command, command-light}
# model_provider: one of {anthropic, cohere, google, openai, azure}
export model_name=<your_model_name> \ 
export model_provider=<your_model_provider>
python src/run_scratch.py \
++experiments.agent_1.model_name=$model_name \
++experiments.agent_1.model_provider=$model_provider \
++experiments.agent_2.model_name=$model_name \
++experiments.agent_2.model_provider=$model_provider \
++offer_extraction_model_name=$model_name \
++offer_extraction_model_provider=$model_provider \
++max_rounds=1

Tada! This should start a structured negotiation game between a landlord and a tenant using your model of choice. For a more elaborate example, e.g., on how to change the games/issues/issues_weights as well as the agent descriptions, please check out the example.ipynb notebook.

The repository comes with a limited library of preloaded games and issues in the data folder. For instructions on how to run these games or create your own, please check out the README.md guide contained in this repository under data/.

For any questions, feel free to open a ticket or reach out directly to Tim or Venia :).

Note on Google/MSFT Azure

If you are using Google or MSFT Azure, you also need to update the relevant endpoints in data/api_settings/apis.yaml. At the time of release, the Google Vertex API does not support simple API keys in all regions. To get around this, you have to create a (1) service account, (2) set some permissions, (3) download a .json. Save the exported .json file in a file called gcp_secrets.json in the root directory of this project (also in .gitignore). See the following docs for a walkthrough.

License

MIT

Citation

Please cite our work using one of the following if you end up using the repository - thanks!

[1] T.R. Davidson, V. Veselovsky, M. Josifoski, M. Peyrard, A. Bosselut, M. Kosinski, R. West. 
Evaluating Language Model Agency through Negotiations. ICLR, 2024.

BibTeX format:

@article{davidson2024evaluating,
      title={Evaluating Language Model Agency through Negotiations}, 
      author={Tim R. Davidson and 
              Veniamin Veselovsky and 
              Martin Josifoski and 
              Maxime Peyrard and 
              Antoine Bosselut and 
              Michal Kosinski and 
              Robert West},
      year={2024},
      journal={ICLR}
}