FANToM is a benchmark designed to test Theory of Mind (ToM) in language models (LLMs) through conversational question-answering. It assesses LLMs’ ability to understand others’ mental states and track beliefs in discussions using 10,000 questions based on multiparty conversations with information asymmetry. The evaluation results reveal that existing LLMs perform worse than humans on FANToM, highlighting the challenges in developing models with coherent ToM reasoning. Future research may include incorporating pragmatics, visual information, and belief graphs to improve ToM understanding in LLMs. FANToM is publicly available for further research.
Meet FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions
In conversational AI, evaluating the Theory of Mind (ToM) through question-answering has become an essential benchmark. However, passive narratives need to improve in assessing ToM capabilities. To address this limitation, diverse questions have been designed to necessitate the same reasoning skills. These questions have revealed the limited ToM capabilities of LLMs. Even with chain-of-thought reasoning or fine-tuning, state-of-the-art LLMs still require assistance when dealing with these questions and perform below human standards.
Researchers from different universities introduced FANToM, a benchmark for testing ToM in LLMs through conversational question answering. It incorporates psychological and empirical insights into LLM evaluation. FANToM proves challenging for top LLMs, which perform worse than humans even with advanced reasoning or fine-tuning. The benchmark evaluates LLMs by requiring binary responses to questions about characters’ knowledge and listing characters with specific information. Human performance was assessed with 11 student volunteers.
Key Features of FANToM:
- Designed to assess machine ToM in conversational contexts
- Focuses on social interactions
- Includes 10,000 questions within multiparty conversations
- Emphasizes information asymmetry and distinct mental states among characters
- Measures models’ ability to track beliefs in discussions
- Tests understanding of others’ mental states and identifies instances of illusory ToM
The evaluation results of FANToM reveal that even with chain-of-thought reasoning or fine-tuning, existing LLMs perform significantly worse than humans. Some LLM ToM reasoning in FANToM is deemed illusory, indicating their inability to comprehend distinct character perspectives. While applying zero-shot chain-of-thought logic or fine-tuning improves LLM scores, substantial gaps compared to human performance persist. The findings underscore the challenges in developing models with coherent Theory of Mind reasoning, emphasizing the difficulty of achieving human-level understanding in LLMs.
In conclusion, FANToM is a valuable benchmark for assessing ToM in LLMs during conversational interactions, highlighting the need for more interaction-oriented standards that align better with real-world use cases. The measure has shown that current LLMs underperform compared to humans, even with advanced techniques. It has identified the issue of internal consistency in neural models and provided various approaches to address it. FANToM emphasizes distinguishing between accessible and inaccessible information in ToM reasoning.
Future Research Directions:
- Grounding ToM reasoning in pragmatics, visual information, and belief graphs
- Expanding evaluations to diverse conversation scenarios beyond small talk
- Integrating multi-modal aspects like visual information
- Addressing the issue of internal consistency in neural models
- Incorporating relationship variables for more dynamic social reasoning
FANToM is now publicly available for further research, promoting the advancement of ToM understanding in LLMs. Future studies may consider incorporating relationship variables for more dynamic social reasoning.
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