Understanding In-context Learning of Addition via Activation Subspaces
Quick summary
arXiv:2505.05145v4 Announce Type: replace-cross Abstract: To perform few-shot learning, language models extract signals from a few input-label pairs, aggregate them into a learned prediction rule, and apply this rule to new inputs. How is this implemented in the forward pass of modern transformer models? To explore this question, we study a structured family of few-shot learning tasks for which the true prediction rule is to add an integer $k$ to the input. We introduce a novel method that localizes the model's few-shot learning ability to only a few attention heads. This method and the findin
Key takeaways
- arXiv:2505.05145v4 Announce Type: replace-cross Abstract: To perform few-shot learning, language models extract signals from a few input-label pairs, aggregate them into a learned prediction rule, and apply this rule to new inputs.
- How is this implemented in the forward pass of modern transformer models?
- To explore this question, we study a structured family of few-shot learning tasks for which the true prediction rule is to add an integer $k$ to the input.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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