Learning Evidence Highlighting for Frozen LLMs

The paper introduces HiLight, an Evidence Emphasis framework designed to enhance the performance of frozen Large Language Models (LLMs) by decoupling evidence selection from reasoning. HiLight employs a lightweight Emphasis Actor that uses reinforcement learning to insert highlight tags around critical spans in the input without altering the original text, leading to improved performance in tasks like sequential recommendation and long-context question answering. This approach demonstrates zero-shot transferability across different Solver architectures, indicating its potential for broader applicability in enhancing LLMs without requiring task-specific evidence labels.