Summary of Conditional and Modal Reasoning in Large Language Models, by Wesley H. Holliday and Matthew Mandelkern and Cedegao E. Zhang
Conditional and Modal Reasoning in Large Language Models
by Wesley H. Holliday, Matthew Mandelkern, Cedegao E. Zhang
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
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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 distinguish logically correct from fallacious inferences, focusing on conditionals and epistemic modals. Twenty-nine LLMs were tested on various inference patterns, revealing that all models make basic mistakes with conditionals or modals. While zero-shot chain-of-thought prompting improves performance, even the best-performing LLMs exhibit logical inconsistencies and errors in modal reasoning, deviating from human judgments. This study highlights gaps in basic logical reasoning in today’s LLMs, underscoring the need for further research to bridge the gap between human and AI reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models can make smart decisions. It tested 29 of these models on different ways of thinking about possibilities. The results showed that even the best models get some things wrong, like making logical mistakes or giving answers that don’t match what humans would say. This study helps us understand where AI is falling short in terms of being able to reason like humans do. |
Keywords
» Artificial intelligence » Inference » Prompting » Zero shot
