Re-examining learning linear functions in context

• URL: http://arxiv.org/abs/2411.11465v3
• Date: Tue, 24 Dec 2024 09:03:02 GMT
• Title: Re-examining learning linear functions in context
• Authors: Omar Naim, Guilhem Fouilhé, Nicholas Asher,
• Abstract summary: In-context learning (ICL) has emerged as a powerful paradigm for easily adapting Large Language Models (LLMs) to various tasks. We explore a simple model of ICL in a controlled setup with synthetic training data. Our findings challenge the prevailing narrative that transformers adopt algorithmic approaches to learn a linear function in-context.
• Score: 1.8843687952462742
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• Abstract: In-context learning (ICL) has emerged as a powerful paradigm for easily adapting Large Language Models (LLMs) to various tasks. However, our understanding of how ICL works remains limited. We explore a simple model of ICL in a controlled setup with synthetic training data to investigate ICL of univariate linear functions. We experiment with a range of GPT-2-like transformer models trained from scratch. Our findings challenge the prevailing narrative that transformers adopt algorithmic approaches like linear regression to learn a linear function in-context. These models fail to generalize beyond their training distribution, highlighting fundamental limitations in their capacity to infer abstract task structures. Our experiments lead us to propose a mathematically precise hypothesis of what the model might be learning.

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