👻 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

We report evaluation results for newer models on the short conversation inputs. Scores are expected to be lower on the full conversation inputs. The models are ranked according to the All* score and then the All score, and ties are broken based on the sum of All AnswerabilityQ and All Info-AccessQ scores.

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-4o-2024-05-13	0.8	2.0	49.7	17.1	36.2	12.0	41.7	65.5	17.0	21.7	86.9	46.9
GPT-4-0125-preview	0.6	1.6	27.6	13.8	38.4	19.2	46.0	69.6	8.4	17.2	77.1	49.4
Gemini-1.5-flash	0.6	1.4	44.4	37.9	27.6	10.3	39.0	59.7	8.6	14.2	75.2	44.9
GPT-4-turbo-2024-04-09	0.3	1.6	42.6	12.5	34.0	21.5	46.6	70.5	6.4	13.4	75.7	45.8
Llama-3-70b-chat-hf	0.3	0.6	73.8	40.3	37.4	7.8	21.4	67.4	1.7	2.7	82.6	54.9
Mixtral-8x22B-Inst-v0.1	0.2	0.5	36.7	32.3	46.4	3.9	24.0	54.5	12.9	20.9	80.3	58.9
Gemini-1.0-pro	0.2	0.2	25.8	65.6	42.0	0.2	18.1	16.8	16.5	32.9	77.7	47.6
Mixtral-8x7B-Inst-v0.1	0.2	0.2	35.4	9.3	46.0	1.4	28.5	40.4	3.7	17.9	52.4	54.1
GPT-3.5-turbo-0125	0.2	0.2	10.1	11.0	45.0	3.3	41.2	56.8	1.1	32.3	61.7	55.4
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
Claude-3-Opus-20240229	0.2	0.2	79.2	24.0	29.7	0.8	6.1	52.4	0.6	0.8	82.6	34.4
Llama-3-8B-chat-hf	0.2	0.2	57.8	53.7	16.1	0.3	7.8	41.3	0.8	5.9	65.8	21.5
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
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
Claude-3-Sonnet-20240229	0.0	0.0	45.1	24.4	32.4	0.5	17.2	24.7	1.2	3.0	70.2	34.9
Gemma 7B Instruct	0.0	0.0	44.4	19.4	30.7	0.2	3.9	51.5	0.8	15.9	56.1	26.3

Results on the Control Task

Our code additionally evaluates performance on the control task, which consists of conversations without information asymmetry (i.e., task without false belief). The results will be dumped under data/results. You can see the models perform significantly better on these than on the original task involving information asymmetry (i.e., task involving false belief). Moreover, the performance gap between models is much smaller. Please ensure that the performance on the control task remains stable when testing your method or model.

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
GPT-4o-2024-05-13	44.0	45.5	88.3	94.1	42.3	17.5	76.0	63.9	59.4	90.8	91.6	46.9
GPT-4-turbo-2024-04-09	17.1	18.4	87.6	95.8	38.6	23.1	62.4	69.3	76.9	86.9	97.1	45.8
Llama-3-70b-chat-hf	17.1	18.4	85.6	91.0	45.0	28.4	82.5	72.6	56.3	94.8	90.9	54.9
GPT-4-0125-preview	16.7	18.0	93.6	96.2	43.7	23.1	68.1	71.8	76.9	82.1	98.0	49.4
GPT-3.5-turbo-0125	14.0	14.0	96.0	96.5	48.4	15.7	34.5	74.3	67.7	69.0	99.4	55.4
Gemini-1.5-flash	10.1	12.7	87.9	86.1	34.3	14.4	62.9	63.1	79.9	91.7	96.4	44.9
Mixtral-8x22B-Inst-v0.1	3.5	3.5	85.7	90.7	49.5	4.8	50.2	43.3	67.7	84.7	94.7	58.9
Claude-3-Opus-20240229	1.3	3.9	71.3	85.9	30.0	5.7	85.6	40.9	73.4	96.9	95.8	34.4
Llama-3-8B-chat-hf	0.9	1.8	92.1	74.4	21.1	2.6	74.7	33.9	36.2	82.1	81.5	21.5
Claude-3-Sonnet-20240229	0.4	0.4	81.4	87.2	29.5	0.9	82.5	20.5	23.1	96.5	66.8	34.9
Gemini-1.0-pro	0.0	0.0	84.3	63.6	37.3	0.0	19.2	3.0	48.9	70.3	89.0	47.6
Mixtral-8x7B-Inst-v0.1	0.0	0.0	83.9	96.3	48.5	1.7	41.5	32.7	9.6	84.7	54.3	54.1
Gemma 7B Instruct	0.0	0.0	44.1	89.9	37.3	0.0	0.4	67.5	1.7	8.7	73.6	26.3

⚠️ 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.