Summary of Out-of-distribution Generalization Via Composition: a Lens Through Induction Heads in Transformers, by Jiajun Song et al.
Out-of-distribution generalization via composition: a lens through induction heads in Transformers
by Jiajun Song, Zhuoyan Xu, Yiqiao Zhong
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 Large language models (LLMs) like GPT-4 demonstrate creative problem-solving skills by generalizing novel tasks from a few demonstrations. This phenomenon, known as out-of-distribution (OOD) generalization, occurs when models adapt to distributions different from their training data. Despite LLMs’ impressive performance, the mechanisms driving OOD generalization remain poorly understood. Our research delves into this mystery by investigating OOD generalization in scenarios where instances are generated according to hidden rules. We explore symbolic reasoning and in-context learning methods to enable models to infer these hidden rules without fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how big language models, like GPT-4, can learn new things when shown just a few examples. These models seem creative because they can figure out new problems from scratch. But we don’t fully understand how they do this. Our research looks at how these models handle situations where the rules aren’t obvious. We’re trying to find out what makes them so good at learning new things. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Gpt
