Home Community Meet FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

Meet FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

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 grow to be a necessary benchmark. Nonetheless, passive narratives need to enhance in assessing ToM capabilities. To handle this limitation, diverse questions have been designed to necessitate the identical 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 coping with these questions and perform below human standards.

Researchers from different universities introduced FANToM, a benchmark for testing ToM in LLMs through conversational query answering. It incorporates psychological and empirical insights into LLM evaluation. FANToM proves difficult 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 on characters’ knowledge and listing characters with specific information. Human performance was assessed with 11 student volunteers.

FANToM is a brand new English benchmark designed to evaluate machine ToM in conversational contexts, specializing in social interactions. It includes 10,000 questions inside multiparty conversations, emphasizing information asymmetry and distinct mental states amongst characters. The goal is to measure models’ ability to trace beliefs in discussions, testing their understanding of others’ mental states and identifying instances of illusory ToM. 

FANToM tests machine ToM in LLMs through question-answering in conversational contexts with information asymmetry. It includes 10,000 questions based on multiparty conversations where characters have distinct mental states attributable to inaccessible information. The benchmark assesses LLMs’ ability to trace beliefs in discussions and discover illusory ToM. Despite chain-of-thought reasoning or fine-tuning, existing LLMs perform significantly worse on FANToM than humans, as evaluated results indicate.

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 grasp distinct character perspectives. While applying zero-shot chain-of-thought logic or fine-tuning improves LLM scores, substantial gaps in comparison with human performance persist. The findings underscore the challenges in developing models with coherent Theory of Mind reasoning, emphasizing the problem of achieving human-level understanding in LLMs.

In conclusion, FANToM is a beneficial benchmark for assessing ToM in LLMs during conversational interactions, highlighting the necessity for more interaction-oriented standards that align higher with real-world use cases. The measure has shown that current LLMs underperform in comparison with humans, even with advanced techniques. It has identified the difficulty of internal consistency in neural models and provided various approaches to handle it. FANToM emphasizes distinguishing between accessible and inaccessible information in ToM reasoning. 

Future research directions include grounding ToM reasoning in pragmatics, visual information, and belief graphs. Evaluations can encompass diverse conversation scenarios beyond small talk on specific topics, and multi-modal elements like visual information might be integrated. Addressing the difficulty of internal consistency in neural models is crucial. 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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Hello, My name is Adnan Hassan. I’m a consulting intern at Marktechpost and shortly to be a management trainee at American Express. I’m currently pursuing a dual degree on the Indian Institute of Technology, Kharagpur. I’m enthusiastic about technology and wish to create latest products that make a difference.

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