Summary of Conditions For Length Generalization in Learning Reasoning Skills, by Changnan Xiao and Bing Liu
Conditions for Length Generalization in Learning Reasoning Skills
by Changnan Xiao, Bing Liu
First submitted to arxiv on: 22 Nov 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 paper investigates the limitations of large language models (LLMs) in performing reasoning tasks, particularly their inability to generalize to longer problems when trained on shorter ones. Despite their impressive abilities, LLMs struggle with length generalization, indicating potential theoretical limitations. To address this issue, the authors focus on reasoning tasks formulated as Markov dynamic processes (MDPs) and/or directed acyclic graphs (DAGs), identifying conditions that determine whether a task can be solved or not in a specific representation. Theoretical results are verified through experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well big language models can solve problems by using reasoning. It finds that these models have trouble solving longer problems even if they’re trained on shorter ones. This is important because it could mean there’s something fundamental about how these models learn that makes them bad at generalizing to new, longer problems. |
Keywords
* Artificial intelligence * Generalization
