Papers by Yilu Hu

1 papers
Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning (2026.findings-acl)

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Challenge: Existing approaches aggregate demonstrations from all classes into a shared, task-level context vector, capturing global task information but without explicitly preserving fine-grained, class-conditional semantic distinctions.
Approach: They propose a class-conditional context vector extension to implicit in-context learning that explicitly models class-specific contextual information by constructing separate context vectors for each class.
Outcome: The proposed extension outperforms task-level context vector baselines and achieves higher average accuracy than conventional few-shot learning on most models.

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