Summary of A Controlled Study on Long Context Extension and Generalization in Llms, by Yi Lu et al.
A Controlled Study on Long Context Extension and Generalization in LLMs
by Yi Lu, Jing Nathan Yan, Songlin Yang, Justin T. Chiu, Siyu Ren, Fei Yuan, Wenting Zhao, Zhiyong Wu, Alexander M. Rush
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposed study investigates language models that utilize full document contexts for broad textual understanding and in-context learning. To compare various extension methods for handling long contexts, the authors implement a controlled protocol with standardized evaluation using consistent base models and extension data. The findings highlight the importance of perplexity as a performance indicator, the underperformance of approximate attention methods across long-context tasks, and the effectiveness of exact fine-tuning based methods within their range of extension. The study also notes the challenges of extrapolation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to make language models better at understanding longer pieces of text. It compares different techniques for doing this and finds that some work better than others. The main idea is that we need better ways to understand text that uses many words, not just a few sentences. The study also shows that using a certain kind of attention in the model doesn’t work as well when the context is longer. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Perplexity
