Summary of Sometimes I Am a Tree: Data Drives Unstable Hierarchical Generalization, by Tian Qin et al.
Sometimes I am a Tree: Data Drives Unstable Hierarchical Generalization
by Tian Qin, Naomi Saphra, David Alvarez-Melis
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 how language models (LMs) generalize out-of-distribution (OOD) when applying grammatical rules. Unlike n-gram models, LMs must learn hierarchical syntactic representations to accurately apply these rules. The authors explore how complex training data drives models to generalize OOD, using case studies of English grammar. They introduce a framework that connects random variation with training dynamics, rule selection with memorization, and data diversity with complexity. The study reveals that these factors are nuanced, and intermediate levels of diversity and complexity lead to inconsistent behavior across random seeds and unstable training dynamics. The findings highlight the crucial role of training data in shaping generalization patterns and illustrate how competing model strategies result in inconsistent generalization outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models learn to follow rules they haven’t seen before. It’s like a student trying to apply what they learned from a book to new situations. The researchers want to know why these models sometimes make mistakes when faced with unfamiliar rules. They study how different types of training data affect the model’s ability to generalize, or apply its knowledge to new cases. The findings show that the quality and complexity of the training data are important in determining whether the model can successfully apply grammatical rules out-of-distribution. |
Keywords
» Artificial intelligence » Generalization » N gram
