Summary of Re-examining Learning Linear Functions in Context, by Omar Naim et al.
Re-examining learning linear functions in context
by Omar Naim, Guilhem Fouilhé, Nicholas Asher
First submitted to arxiv on: 18 Nov 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 The abstract discusses the in-context learning (ICL) paradigm for adapting Large Language Models (LLMs) to various tasks, but notes that our understanding of how ICL works remains limited. The authors explore a simple model of ICL using synthetic training data and GPT-2-like transformer models trained from scratch. Their findings challenge the prevailing narrative that transformers adopt algorithmic approaches like linear regression to learn a linear function in-context, instead highlighting fundamental limitations in their capacity to infer abstract task structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ICL is a way to teach Large Language Models (LLMs) new tasks without needing them to be re-trained from scratch. But scientists don’t fully understand how this works. Researchers created a simple test case using fake training data and tried it with different GPT-2-like models. They found that these models don’t really learn the underlying rules of a task, but instead just memorize what they’ve seen before. This shows there are big limitations in their ability to figure out what’s going on. |
Keywords
» Artificial intelligence » Gpt » Linear regression » Transformer
