Summary of Disentangling Logic: the Role Of Context in Large Language Model Reasoning Capabilities, by Wenyue Hua et al.
Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities
by Wenyue Hua, Kaijie Zhu, Lingyao Li, Lizhou Fan, Shuhang Lin, Mingyu Jin, Haochen Xue, Zelong Li, JinDong Wang, Yongfeng Zhang
First submitted to arxiv on: 4 Jun 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 The study aims to distinguish between pure logical reasoning and text understanding by analyzing abstract and contextualized logical problems across various domains. Researchers investigate whether large language models (LLMs) demonstrate genuine reasoning capabilities when the underlying logical structure remains constant, focusing on standard propositional logic, including deductive and abductive reasoning. The study constructs datasets for deductive and abductive reasoning with 4 levels of difficulty, covering 12 distinct categories or domains based on Wikipedia categorization. Experiments aim to provide insights into disentangling context in logical reasoning and the true reasoning capabilities of LLMs and their generalization potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well computers can reason logically by giving them problems from different areas like science, history, and more. The scientists want to know if the computer’s ability to solve these problems is because it’s good at logic or just because it’s learned from context. They’re also looking at whether fine-tuning the computer on one type of problem helps it with similar problems in a different area. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
