Summary of Revisiting In-context Learning with Long Context Language Models, by Jinheon Baek et al.
Revisiting In-Context Learning with Long Context Language Models
by Jinheon Baek, Sun Jae Lee, Prakhar Gupta, Geunseob Oh, Siddharth Dalmia, Prateek Kolhar
First submitted to arxiv on: 22 Dec 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 This paper explores In-Context Learning (ICL), a technique where language models make predictions based on provided input context. Previously, the limited context window size made selecting effective examples crucial. However, Long Context Language Models (LCLMs) have increased the number of examples that can be included in context, raising questions about ICL performance with many-shot regimes. The authors revisit example selection techniques through extensive experiments on 18 datasets across four tasks using LCLMs. Surprisingly, they find that sophisticated methods do not improve performance over simple random sampling. Instead, the challenge shifted from selecting effective examples to collecting sufficient examples to fill the context window. By augmenting examples with a data augmentation approach, they improved ICL performance by 5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how language models learn new things based on what’s around them in their training data. Before, there was a limit to how many examples they could use, so choosing the right ones was important. But now that we have bigger training datasets, it seems like the model doesn’t need as much help with picking examples anymore. In fact, just using all the available examples doesn’t always work well, but if you add some extra information to those examples, you can actually make the model better at learning. |
Keywords
» Artificial intelligence » Context window » Data augmentation
