Verb-ICL: Rethinking In-Context Learning for Structured Prediction
Structured prediction tasks pose unique challenges for in-context learning (ICL): their compositional outputs require modeling fine-grained, token-level patterns that sentence-level approaches fail to capture, and their task-specific annotation conventions are human-defined artifacts that cannot be acquired through pretraining alone. We propose Verb-ICL, a selective annotation framework for ICL-based structured prediction that addresses both challenges. Verb-ICL first selects representative examples using a token-level coverage strategy that captures local semantic patterns critical for structured prediction, then generates actionable error feedback that codifies task-specific annotation ...
| Authors | Fan Bai, Hengshuo Miao, Sanjit S Batra et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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