HiLight: A Framework for Highlighting Evidence in Long Contexts for Large Language Models
Researchers introduced HiLight, a framework that trains a lightweight model to highlight important evidence in long texts without modifying the original content, allowing frozen LLMs to reason more effectively. The approach uses reinforcement learning to train an "Emphasis Actor" to insert highlight tags around key information, requiring no manual evidence labels. The method shows consistent improvements on sequential recommendation and question-answering tasks and transfers effectively to different LLM sizes and types.
HiLight addresses a known limitation of large language models: their difficulty in identifying and using crucial evidence when it appears within lengthy, noisy contexts. Rather than compressing or rewriting input text—which risks losing or distorting important information—the framework trains a lightweight Emphasis Actor to mark pivotal spans with minimal highlight tags while preserving the original context intact. A frozen Solver LLM then performs reasoning on the emphasized input. The system frames highlighting as a weakly supervised decision-making problem and optimizes the Actor using reinforcement learning with only the Solver's task reward, eliminating the need for manual evidence annotations or modifications to the underlying LLM. Evaluation across sequential recommendation and long-context question-answering tasks demonstrates consistent performance improvements over prompt-based and automated prompt-optimization baselines. Notably, the learned emphasis policy generalizes zero-shot to both smaller and larger unseen Solver families, including API-based models, suggesting the Actor learns genuine, transferable patterns of evidence structure rather than overfitting to specific model architectures.
What's missing
The paper does not discuss computational overhead or latency costs of the Emphasis Actor during inference, nor does it provide detailed ablation studies on the design choices for highlight tag insertion. Additionally, the specific performance metrics and numerical improvements over baselines are not included in the abstract.
What different sources said
- arXiv cs.AICenter
Learning Evidence Highlighting for Frozen LLMs
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