REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning
Large Language Models (LLMs) increasingly leverage long-form reasoning to solve complex tasks, yet their reasoning processes can deviate from the provided context when evidence is incomplete, noisy, or conflicts with parametric knowledge. Existing grounding approaches either append citations after generation or encourage LLMs to retrieve evidence during reasoning, but they often fail to ensure that cited information is sufficient to support intermediate inferences and final answers. To address this limitation, we propose REFACT, an adaptive fact-restatement citation framework that enables LLMs to determine when contextual grounding is needed and selectively restate source facts at appropr...
| Authors | Zhensheng Jin, Xin Dai, Zhenghao Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 July 2026 |
| Last updated at source | 29 July 2026 |
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