Summary of Zero, Finite, and Infinite Belief History Of Theory Of Mind Reasoning in Large Language Models, by Weizhi Tang et al.
Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models
by Weizhi Tang, Vaishak Belle
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This abstract proposes a novel framework for Theory of Mind (ToM) reasoning in Large Language Models (LLMs). The proposed method, called “ToM Reasoning with Zero, Finite, and Infinite Belief History,” evaluates the LLMs’ performance on a text-based game called “Pick the Right Stuff.” Six LLMs were tested, showing that models with small parameter sizes outperformed those with large parameter sizes. This research aims to pave the way for future ToM benchmark development and more complex AI agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are getting better at understanding human thoughts and feelings! Researchers created a new game to test how well LLMs can think about what others might be thinking or feeling. They found that some smaller models were actually better at this game than bigger ones. This work will help create even more advanced AI systems in the future. |
