Summary of Can Large Language Models Reason? a Characterization Via 3-sat, by Rishi Hazra et al.
Can Large Language Models Reason? A Characterization via 3-SAT
by Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires, Luc De Raedt
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 ability of Large Language Models (LLMs) to perform true reasoning by solving 3-SAT problems, which are a benchmark for logical reasoning. The authors propose an experimental protocol centered on 3-SAT and examine how LLMs reason when faced with varying levels of problem hardness. They find that LLMs cannot truly reason and their performance varies significantly based on the difficulty of the problems, performing poorly on harder instances. However, integrating external reasoners can improve LLM performance. This study provides a principled experimental approach to evaluating LLM reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very smart computers that can do many things. But some people think they might not be as good at “reasoning” – which means figuring out answers based on logic and rules. To find out if this is true, scientists designed a special test to see how well LLMs do at solving complex puzzles called 3-SAT problems. They found that LLMs don’t actually reason when they solve these problems – instead, they use shortcuts or tricks. However, if they get help from other “reasoners”, they can do better. |
