Summary of Metacognitive Capabilities Of Llms: An Exploration in Mathematical Problem Solving, by Aniket Didolkar et al.
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving
by Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy Lillicrap, Danilo Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora
First submitted to arxiv on: 20 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 paper investigates the metacognitive knowledge of large language models (LLMs), specifically whether they possess an understanding of their own thinking and reasoning processes. The study finds that top-performing LLMs are not only capable of exhibiting reasoning processes but also demonstrate metacognitive knowledge, including the ability to identify skills and procedures necessary for a given task. The research focuses on math reasoning, developing a prompt-guided interaction procedure to elicit sensible skill labels from an LLM, followed by semantic clustering to obtain coarser families of skill labels that are interpretable to humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can think and reason like humans do. This study shows that they also know how they’re thinking and reasoning! Researchers used math problems to test this idea. They asked the LLM to identify the skills it needed to solve a problem, then grouped those skills into categories. The results show that the LLM’s answers are understandable to people. |
Keywords
» Artificial intelligence » Clustering » Prompt
