sayontang / fantom

👻 Code and benchmark for our EMNLP 2023 paper - "FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions"

👻 FANToM

This is the official repository of our paper:
FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

Please cite our work if you found the resources in this repository useful:

@inproceedings{kim2023fantom,
    title={FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions},
    author={Hyunwoo Kim and Melanie Sclar and Xuhui Zhou and Ronan Le Bras and Gunhee Kim and Yejin Choi and Maarten Sap},
    booktitle={EMNLP},
    year=2023
}

For a brief summary of our paper, please see this webpage.

💻 Running evaluation on FANToM

​ First, create the conda environment by running:

conda env create -f environment.yml

​ and then activate it:

conda activate fantom

You can run the evaluation by running the following example command:

python eval_fantom.py --model gpt-4-0613

This will automatically download the new FANToM benchmark in data. All evaluation results will be saved under data/results.

Adding your own agent

All you need to do is create an agent class with the method interact() or batch_interact().

📊 Latest Results

These are results on the short conversation inputs. Scores will be worse on the full conversation inputs.

Model	All*	All	BeliefQ [Choice]	BeliefQ [Dist.]	BeliefQ token-F1	All AnswerabilityQ	AnswerabilityQ [List]	AnswerabilityQ [Y/N]	All Info-AccessQ	Info-AccessQ [List]	Info-AccessQ [Y/N]	FactQ token-F1
Human		87.5	93.8			90.6	90.6		90.6	90.6
GPT-4-1106-preview	0.0	0.2	51.9	20.3	33.7	6.6	28.5	50.6	4.7	8.7	77.8	46.2
GPT-3.5-turbo-1106	0.2	0.2	9.6	15.7	41.7	3.4	44.6	62.4	0.3	26.2	59.8	54.3
Zephyr 7B beta	0.0	0.2	41.5	33.2	41.1	0.6	13.6	56.0	0.9	6.1	56.0	37.1

⚠️ Intended Use of Data

The samples in FANToM should only be used for evaluation purposes.

💡 Disclaimer

1. We are not claiming that machines have minds. They do not have minds, emotions, or intentions. However, they do need social reasoning capabilities to better understand information.
2. These multi-party conversations were generated by GPT-4 and they were validated by humans. The conversations do not necessarily reflect the views and opinions of the authors and their associated affiliations.