Summary of Do Llms Exhibit Human-like Reasoning? Evaluating Theory Of Mind in Llms For Open-ended Responses, by Maryam Amirizaniani et al.
Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses
by Maryam Amirizaniani, Elias Martin, Maryna Sivachenko, Afra Mashhadi, Chirag Shah
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) excel in various tasks like summarization, question answering, and translation but struggle with Theory of Mind (ToM) reasoning, particularly in open-ended questions. Despite advancements, the extent to which LLMs truly understand ToM reasoning remains inadequately explored. Our study assesses LLMs’ abilities to perceive and integrate human intentions and emotions into their ToM reasoning processes within open-ended questions using Reddit’s ChangeMyView platform. We compare semantic similarity and lexical overlap metrics between responses generated by humans and LLMs, revealing clear disparities in ToM reasoning capabilities. Even the most advanced models show notable limitations. To enhance LLM capabilities, we implement a prompt tuning method that incorporates human intentions and emotions, resulting in improvements in ToM reasoning performance. However, despite these improvements, the enhancement still falls short of fully achieving human-like reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well large language models (LLMs) can understand people’s thoughts and feelings. LLMs are great at doing tasks like summarizing texts or answering questions, but they struggle with understanding what others might be thinking or feeling. The researchers used a special platform called ChangeMyView to test the LLMs’ abilities. They found that even the best LLMs aren’t very good at this kind of reasoning. To help them get better, the researchers came up with a new way to write prompts for the LLMs that includes more information about people’s thoughts and feelings. This helped the LLMs do a bit better, but they still didn’t match human-level understanding. |
Keywords
» Artificial intelligence » Prompt » Question answering » Summarization » Translation
