用问答式推理生成更准确的文学角色描述
Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books
- 先通过问答形式梳理角色信息，再生成描述
- 在两个数据集上提升描述忠实度和信息量
- 适合需要精准角色分析的叙事应用
角色描述生成对摘要、故事分析和角色驱动模拟等叙事应用至关重要。然而，从长篇叙述（如小说）中生成准确的角色描述极具挑战：模型需追踪动态属性（如关系与事件）、整合分散在文本中的证据，并推断隐含细节。尽管推理增强的大语言模型在诸多基准上表现优异，我们发现，在角色描述生成任务中，禁用内置推理（即空推理轨迹）反而能提升性能。基于此，我们提出一种将推理与生成解耦的训练框架。该方法可在长上下文大模型或分块方法基础上使用，包含一个生成结构化问答推理轨迹的推理模型，以及一个基于该轨迹生成最终角色描述的生成模型。在BookWorm和CroSS两个数据集上的实验表明，问答引导的推理显著提升了描述的忠实度、信息量和事实依据性，优于强基线模型。
原文摘要 · Abstract (English)
Character description generation is an important capability for narrative-focused applications such as summarization, story analysis, and character-driven simulations. However, generating accurate character descriptions from long-form narratives (e.g., novels) is challenging: models must track evolving attributes (e.g., relationships and events), integrate evidence scattered across the text, and infer implicit details. Despite the success of reasoning-enabled LLMs on many benchmarks, we find that for character description generation their performance improves when built-in reasoning is disabled (i.e., an empty reasoning trace). Motivated by this, we propose a training framework that decouples reasoning from generation. Our approach, which can be applied on top of long-context LLMs or chunk-based methods, consists of a reasoning model that produces a structured QA reasoning trace and a generation model that conditions on this trace to produce the final character description. Experiments on two datasets (BookWorm and CroSS) show that QA-guided reasoning improves faithfulness, informativeness, and grounding over strong long-context baselines.
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