Summary of Evaluating the Robustness Of Analogical Reasoning in Large Language Models, by Martha Lewis et al.
Evaluating the Robustness of Analogical Reasoning in Large Language Models
by Martha Lewis, Melanie Mitchell
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 robustness of analogical reasoning abilities claimed for Large Language Models (LLMs) on three domains: letter-string analogies, digit matrices, and story analogies. By testing humans and GPT models on variant analogy problems, the study aims to determine whether LLMs are performing general abstract reasoning or relying too heavily on similarity to pre-training data. The results will help assess the robustness of LLMs’ analogy-making abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research examines if Large Language Models (LLMs) can really reason abstractly like humans. Right now, some LLMs are great at solving analogies and other problems that require thinking ahead. But we’re not sure if they’re doing this because they truly understand the concepts or just because they’ve seen similar problems before. To find out, scientists will test these models on new, tricky analogy puzzles to see how well they do. This will help us understand what LLMs are really good at and where they might need more improvement. |
Keywords
» Artificial intelligence » Gpt
