TELLER：用于表格实体链接的双路径迭代偏好优化
TELLER: Dual-Path Iterative Preference Optimization for Table Entity Linking
- RWTH Aachen（亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对表格实体链接任务提出TELLER双路径迭代偏好优化方法，通过直接回答与推理两条路径分别优化，在TableInstruct和MammoTab V2数据集上提升了实体链接与推理生成的准确率。
AI中文摘要：
表格中的实体链接是将简短且含义模糊的单元格提及与其对应的知识库实体进行匹配的任务。现有方法通常依赖数据预处理管道，该管道会保留紧凑或广泛的表格内容作为上下文证据，随后将实体链接构建为针对指令微调模型的语言生成任务；近期的系统还会融入显式推理来消除具有挑战性的提及的歧义。然而，它们的训练监督通常是静态的：固定的偏好数据无法适配不断演进的模型的残余误差，而推理长度的变化会对序列级偏好学习产生偏差。为解决这些限制，我们提出TELLER：通过从误差和推理中学习来实现表格实体链接。我们首先检索并对Wikidata候选实体进行排序，并在提示中保留简化的表格证据。直接回答路径应用迭代直接偏好优化，并使用更新后模型的残余误差刷新其偏好数据；推理路径使用过滤和压缩的思维链理由进行监督微调，随后应用我们的迭代长度归一化正则化偏好优化。在TableInstruct实体链接子集上，直接回答路径将准确率从94.35%提升至94.50%；在MammoTab V2评估集上，其准确率从87.59%提升至88.20%。推理路径在TableInstruct上将准确率从92.90%提升至92.95%，在MammoTab V2上从79.09%提升至81.85%，同时保持高比例的完整推理生成。这些结果表明，迭代偏好学习既有益于简洁的实体预测，也有益于显式推理。
英文摘要：
Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities. Existing approaches typically rely on data preprocessing pipelines that retain either compact or extensive table content as contextual evidence, and then formulate entity linking as a language generation task for instruction-tuned models; recent systems further incorporate explicit reasoning to disambiguate challenging mentions. However, their training supervision is usually static: fixed preference data cannot adapt to the residual errors of an evolving model, while variations in reasoning length can bias sequence-level preference learning. To address these limitations, we present TELLER: Table Entity Linking through Learning from Errors and Reasoning. We first retrieve and rank Wikidata candidates and retain reduced table evidence in the prompt. The direct-answer path applies iterative direct preference optimization and refreshes its preference data with residual errors from the updated model. The reasoning path uses filtered and compressed chain-of-thought rationales for supervised fine-tuning, followed by our iterative length-normalized regularized preference optimization. On the TableInstruct entity-linking subset, the direct-answer path improves accuracy from 94.35\% to 94.50\%; on the MammoTab V2 evaluation set, it improves accuracy from 87.59\% to 88.20\%. The reasoning path improves accuracy from 92.90\% to 92.95\% on TableInstruct and from 79.09\% to 81.85\% on MammoTab V2, while maintaining high rates of complete reasoning generation. These results show that iterative preference learning benefits both concise entity prediction and explicit reasoning.
