m&m's: A Benchmark to Evaluate Tool-Use Agents for multi-step multi-modal Tasks

🌐 Website | 🤗 Dataset | 📖 Paper

m&ms is a benchmark for evaluating large language model (LLM) agents' tool-use abilities on multi-step multi-modal tasks.

Dataset examples

Dataset details

This dataset contains 4K+ multi-step multi-modal tasks involving 33 tools that include 13 multi-modal models, 9 (free) public APIs, and 11 image processing modules. For each of these task queries, we provide automatically generated plans using this realistic toolset. We further provide a high-quality subset of 1,565 human-verified task plans and 882 human-verified, filtered, and correctly executable plans. Below is a table summarizing the statistics of m&ms:

And a table listing all 33 available tools in our tool API:

Dataset generation

Setup

Installation

Please make sure you install all the required packages in requirements.txt by running:

pip install -r requirements.txt

Environment variables

You can set up your environment variables by running the command lines below line by line or store them locally in a file (e.g. .bashrc)

export HUGGINGFACE_HUB_CACHE=<your_cache_dir>
export RAPID_API_KEY=<your_rapid_key>
export OMDB_API_KEY=<your_omdb_key>
export OPENAI_API_KEY=<your_openai_key> 
export GOOGLE_API_KEY=<your_google_key> (only if you experiment with Google's models)
export DEEPINFRA_API_KEY=<your_deepinfra_key> (only if you run models hosted on DeepInfra)

And then run this line for the changes to take effect:

source .bashrc

You can generate your own API keys on Rapid, OMDb, OpenAI, Google, and DeepInfra.

Planning

To run planning experiments on m&ms with LLM agents, below is an example command line

python -m mnms.run_plan_agent  --action-format json --plan-mode multi-step --model gemini-pro --max-reply 10 --verify --execute --exp-id 0331 

where the arguments:

• action-format specifies the output format of the LLM agent i.e. json or code;
• plan-mode refers to the planning strategy multi-step or step-by-step;
• max-reply is the max number of turns the LLM agent can take to refine its initial plan (max-reply=0 means no iterative refinement);
• verifiy and execute specify whether to turn on verification and execution feedback during planning.

Additionally, you can also run experiments on your own queries by adding --input-file <your_input_file>, where each example is expected to contain id and user_request.

Evaluation

To evaluate the predicted plans against the groundtruth plans in m&ms, simply run this line:

python -m mnms.evaluation.run --plan-format json --preds-file <predictions.json> --output-csv <output.csv> 

Note that our code works with a predictions.json file where each line is a dictionary containing "id" and "prediction" like the example below:

{"id": 10, "prediction": [{"id": 0, "name": "text classification", "args": {"text": "forced, familiar and thoroughly condescending."}}]}
{"id": 24, "prediction": [{"id": 0, "name": "text generation", "args": {"text": "Who exactly became the King of the Canary Islands?"}}]}
...

But you are welcome to modify it to work with other file formats.

Execution

To execute the groundtruth plans, you can run this line:

python -m mnms.execution.run --plan-format code

To execute predicted plans, you will need to additionally provide a predictions.json file like this:

python -m mnms.execution.run --plan-format json --input-file <predictions.json>

Citation

If you find our work helpful, please consider starring our repo or citing our paper. Thanks!

@article{ma2024mms,
  title={m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks}, 
  author={Zixian Ma and Weikai Huang and Jieyu Zhang and Tanmay Gupta and Ranjay Krishna},
  year={2024},
  journal={arXiv preprint arXiv:2403.11085},
}