EDA

EDA Evaluation

This folder contains evaluation harness for evaluating agents on the Entity-deduction-Arena Benchmark, from the paper Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games, presented in ACL 2024 main conference.

Configure OpenDevin and your LLM

Create a config.toml file if it does not exist at the root of the workspace. Please check README.md for how to set this up.

Start the evaluation

export OPENAI_API_KEY="sk-XXX"; # This is required for evaluation (to simulate another party of conversation)
./evaluation/EDA/scripts/run_infer.sh [model_config] [git-version] [agent] [dataset] [eval_limit]

where model_config is mandatory, while git-version, agent, dataset and eval_limit are optional.

• model_config, e.g. eval_gpt4_1106_preview, is the config group name for your LLM settings, as defined in your config.toml.

• git-version, e.g. HEAD, is the git commit hash of the OpenDevin version you would like to evaluate. It could also be a release tag like 0.6.2.

• agent, e.g. CodeActAgent, is the name of the agent for benchmarks, defaulting to CodeActAgent.

• dataset: There are two tasks in this evaluation. Specify dataset to test on either things or celebs task.

• eval_limit, e.g. 10, limits the evaluation to the first eval_limit instances. By default it infers all instances.

For example,

./evaluation/EDA/scripts/run_infer.sh eval_gpt4o_2024_05_13 0.6.2 CodeActAgent things

Reference

@inproceedings{zhang2023entity,
  title={Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games},
  author={Zhang, Yizhe and Lu, Jiarui and Jaitly, Navdeep},
  journal={ACL},
  year={2024}
}