用思维链蒸馏训练表格重排模型，显著提升检索准确率。
TabRank: Chain-of-Thought Distillation for Table Re-Rankers
- 通过思维链蒸馏让小模型学习大模型的推理过程
- 在多个数据集上提升13%至53%的排序准确率
- 特别适合需要多表推理的复杂问答场景
从结构化数据中检索相关表格是回答问题的关键任务。多阶段检索系统依赖重排器对第一阶段生成的候选列表进行精炼。由于神经重排器和基于大语言模型的重排方法在语义理解与推理能力上优于传统稀疏或稠密检索模型，因此日益重要。近期具备显式思维链（CoT）推理的大推理模型在非结构化段落检索中表现优异。本文提出TabRank框架，用于训练表格检索的推理型重排器。我们构建了包含6728条推理轨迹的综合性数据集，基于Natural Questions Tables数据集。进一步探索两种在推理轨迹上训练紧凑推理模型的变体：显式思维链蒸馏，以及在提示中引入教师模型的推理轨迹作为条件。在多种分布外泛化设置及多表场景下对TabRank进行压力测试。结果表明，该方法在多个表格检索数据集上均显著提升性能，相比基线模型，在HybridQA上提高Acc@10达30.5%，SQA提升15.2%，TabFact提升52.9%，TATQA子集提升13.1%。尤其在多表推理任务中表现出良好泛化能力。代码、数据与模型已开源。
原文摘要 · Abstract (English)
The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval. Multi-stage retrieval systems rely heavily on rerankers to refine candidate lists produced by efficient first-stage retrievers. As a result, neural rerankers and LLM-based reranking methods have become increasingly important due to their superior capacity for semantic understanding and reasoning compared to conventional sparse or dense retrieval models. Recently, Large Reasoning Models (LRMs) equipped with explicit chain-of-thought (CoT) reasoning have shown strong improvements in ranking quality in unstructured passage retrieval. In this work, we present TabRank, a framework for training reasoning rerankers for Tabular Retrieval. We first present a comprehensive dataset of 6728 reasoning traces for tabular reranking on the Natural Questions Tables dataset. We then explore two variants of training a compact reasoning model on these reasoning traces: explicit CoT distillation and conditioning the student reranker on the teacher's reasoning trace within the prompt. We stress-test TabRank on several out-of-distribution generalization settings on diverse domains and multi-table scenarios. Our approach significantly improves performance across a variety of table retrieval datasets, increasing Acc@10 by 30.5% on HybridQA, 15.2% on SQA, 52.9% on TabFact, and 13.1% on TATQA subsets of the Multi-Table QA Benchmark compared to the base model. Notably, TabRank generalizes effectively to multi-table reasoning. Our code, data and models are available at https://github.com/AdarshSingh7647/TabRanker
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