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AI Research
arXiv CS.AI·Jun 10·5 min read
Learning Evidence Highlighting for Frozen LLMs
TL;DR
Large Language Models (LLMs) often struggle to identify crucial evidence in lengthy and complex texts. HiLight is introduced as an Evidence Emphasis framework that uses a lightweight Emphasis Actor to highlight important spans without altering the original context.
✦ Why It Matters
Engineers can leverage HiLight to improve LLM performance in applications requiring evidence extraction from complex texts.
Key Takeaways
- Use HiLight to enhance evidence selection in LLMs.
- Train the Emphasis Actor with reinforcement learning for better results.
- Apply the framework to various LLM architectures seamlessly.
- Improve performance in sequential recommendation and question answering tasks.
How It Works
HiLight employs a two-step process: first, the Emphasis Actor identifies and highlights critical evidence in the input text without altering it. This is achieved through reinforcement learning, where the Actor learns to maximize task rewards from the Solver, effectively decoupling evidence selection from the reasoning process.