Summary of Comparing Inferential Strategies Of Humans and Large Language Models in Deductive Reasoning, by Philipp Mondorf and Barbara Plank
Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning
by Philipp Mondorf, Barbara Plank
First submitted to arxiv on: 20 Feb 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 Recent advancements in large language models (LLMs) have shown their capabilities in executing deductive reasoning tasks. However, existing research primarily focuses on assessing LLMs’ accuracy in solving such tasks, neglecting a deeper analysis of their reasoning behavior. This study draws upon cognitive psychology principles to investigate the inferential strategies employed by LLMs when responding to propositional logic problems. Our findings indicate that LLMs exhibit reasoning patterns similar to those observed in humans, including supposition following and chain construction. Moreover, we demonstrate that model architecture and scale significantly impact its preferred method of reasoning, with more advanced models adopting these strategies more frequently than less sophisticated ones. Notably, our research highlights the distinction between a model’s accuracy (correctness of final conclusion) and the validity of its reasoning process. This underscores the need for more nuanced evaluation procedures in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how large language models (LLMs) think and make decisions. LLMs are computer programs that can understand and generate human-like text. Researchers have found that LLMs are good at solving problems that require logical thinking, just like humans do. The study also shows that more advanced LLMs tend to use different strategies when solving problems than less advanced ones. What’s important is that a model’s accuracy (whether it gives the right answer) doesn’t always mean its reasoning process is valid. This means we need to evaluate models in a more detailed and nuanced way. |
