#instruction-refinement News & Analysis

2 articles tagged with #instruction-refinement. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 287/10

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Prompt Codebooks: Discrete Compositional Optimization for Language Model Instruction Refinement

Researchers introduce Prompt Codebooks (PCO), a new framework for automatic prompt optimization that breaks down instructions into reusable, atomic components rather than treating prompts as fixed strings. The method achieves up to 30% performance gains over baseline approaches while reducing prompt lengths by 14x, enabling more efficient and adaptive language model instruction refinement.

AIBullisharXiv – CS AI · Apr 206/10

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DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition

Researchers introduce DiZiNER, a framework that improves zero-shot named entity recognition by simulating human annotation disagreement processes using multiple LLMs. The approach achieves state-of-the-art results on 14 of 18 benchmarks, closing the performance gap between zero-shot and supervised systems by over 11 percentage points.

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