用多次采样+选择提升文档级信息抽取效果，比贪心解码更优。
Do not be greedy, Think Twice: Sampling and Selection for Document-level Information Extraction
- 通过多次采样生成多个候选输出，再选最优结果。
- 在多个数据集上超越贪心解码和当前最佳方法，提升显著。
- 适合需要高精度抽取的场景，尤其配合推理模型使用。
文档级信息抽取（DocIE）旨在从给定文档中提取出实体、关系和事件等目标信息并填入输出模板。传统做法是使用仅解码器的大型语言模型（LLM）配合贪心解码以避免输出波动。但我们发现，这种波动并非缺陷，而是可利用的资源；当结合推理模型时，采样能产生远优于贪心解码的结果。为此，我们提出 ThinkTwice 框架：先让 LLM 对同一文档生成多个候选模板，再由选择模块选出最合适的。我们设计了两种方法：一种无需标注数据，利用多个输出间的共识进行选择；另一种基于标注数据训练奖励模型实现监督选择。为缓解 DocIE 中高质量推理轨迹稀缺的问题，我们提出一种拒绝采样方法，自动生成包含输出模板与推理路径的银标数据。实验表明，无论是无监督还是监督版本的 ThinkTwice，均持续优于贪心基线及当前最优监督方法。
原文摘要 · Abstract (English)
Document-level Information Extraction (DocIE) aims to produce an output template with the entities, relations, and events of interest occurring in the given document. Standard practices include prompting decoder-only LLMs using greedy decoding to avoid output variability. Rather than treating this variability as a limitation, we show that sampling can produce substantially better solutions than greedy decoding, especially when using reasoning models. We thus propose ThinkTwice, a sampling and selection framework in which the LLM generates multiple candidate templates for a given document, and a selection module chooses the most suitable one. We introduce both an unsupervised method that exploits agreement across generated outputs, and a supervised selection method using reward models trained on labeled DocIE data. To address the scarcity of golden reasoning trajectories for DocIE, we propose a rejection-sampling-based method to generate silver training data that pairs output templates with reasoning traces. Our experiments show the validity of unsupervised and supervised ThinkTwice, consistently outperforming greedy baselines and the supervised state-of-the-art.
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