Summary of Llm the Genius Paradox: a Linguistic and Math Expert’s Struggle with Simple Word-based Counting Problems, by Nan Xu et al.
LLM The Genius Paradox: A Linguistic and Math Expert’s Struggle with Simple Word-based Counting Problems
by Nan Xu, Xuezhe Ma
First submitted to arxiv on: 18 Oct 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 LLMs (Large Language Models) still struggle with basic word-based counting problems, such as counting the number of ’r’s in “strawberry”. Several conjectures exist regarding this deficiency, attributing it to model pretraining and deployment. This paper investigates these conjectures through multiple evaluation settings, exploring transferability of advanced mathematical and coding reasoning capabilities from specialized LLMs to simple counting tasks. Surprisingly, despite specialized LLMs also struggling with counting, the study finds that these models are not inherently deficient in this regard. Instead, engaging reasoning is shown to be the most robust and efficient way to improve LLM performance on word-based counting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have trouble with simple tasks like counting letters in words. Some people think that’s because these models are just not good at this kind of thing, but this paper says that’s not true. It looks at how well different LLMs do on word-counting and finds that even the best ones struggle a bit. The surprising result is that by making LLMs think more critically about counting, they can actually get better at it. |
Keywords
» Artificial intelligence » Pretraining » Transferability
