Minlie Huang, Bosi Wen, Jinfeng Zhou, Jincenzi Wu, Mengting Hu, Zhuang Chen, Gongyao Jiang, Guanqun Bi, Yaru Cao, Yunghwei Lai, Zexuan Xiong
We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| zhchen18/tombench | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| extract_answer | Ran | zhchen18/tombench/get_results.py code served (permissive licence) · get_code("048418fb06a97048") |
| most_common_element | Ran | zhchen18/tombench/get_results.py code served (permissive licence) · get_code("fb24308f1c903d0a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others. Recent research has sparked a debate over whether large language models (LLMs) exhibit a form of ToM. However, existing ToM evaluations are hindered by challenges such as constrained scope, subjective judgment, and unintended contamination, yielding inadequate assessments. To address this gap, we introduce T MBENCH with three key characteristics: a systematic evaluation framework encompassing 8 tasks and 31 abilities in social cognition, a multiple-choice question format to support automated and unbiased evaluation, and a build-from-scratch bilingual inventory to strictly avoid data leakage. Based on T MBENCH, we conduct extensive experiments to evaluate the ToM performance of 10 popular LLMs across tasks and abilities. We find that even the most advanced LLMs like GPT-4 lag behind human performance by over 10% points, indicating that LLMs have not achieved a human-level theory of mind yet. Our aim with T MBENCH is to enable an efficient and effective evaluation of LLMs' ToM capabilities, thereby facilitating the development of LLMs with inherent social intelligence.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.15052")
get_code_for_paper("2402.15052")
have("2402.15052")
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