Table Retrieval (TR) has traditionally been formulated as an ad-hoc retrieval problem, where relevance is primarily determined by topical semantic similarity. With the growing adoption of LLM-based agentic systems, access to structured data is increasingly instruction-driven, where relevance is conditional on explicit content and schema constraints rather than topical similarity alone. We therefore formalize Instruction-Following Table Retrieval (IFTR), a new task that requires models to jointly satisfy topical relevance and fine-grained instruction constraints. We identify two core challenges in IFTR: (i) sensitivity to content scope, such as inclusion and exclusion constraints, and (ii) awareness of schema-grounded requirements, including column semantics and representation granularity--capabilities largely absent in existing retrievers. To support systematic evaluation, we introduce FollowTable, the first large-scale benchmark for IFTR, constructed via a taxonomy-driven annotation pipeline. We further propose a new metric, termed the Instruction Responsiveness Score, to evaluate whether retrieval rankings consistently adapt to user instructions relative to a topic-only baseline. Our results indicate that existing retrieval models struggle to follow fine-grained instructions over tabular data. In particular, they exhibit systematic biases toward surface-level semantic cues and remain limited in handling schema-grounded constraints, highlighting substantial room for future improvements.

翻译：[译摘要] 传统上，表格检索（Table Retrieval，TR）被形式化为一种特设检索任务，其相关性主要由主题语义相似度决定。随着基于大语言模型的智能体系统日益普及，对结构化数据的访问越来越依赖于指令驱动，即相关性取决于显式的内容和模式约束，而非仅凭主题相似度。因此，我们形式化定义了指令遵循式表格检索（Instruction-Following Table Retrieval，IFTR），这是一项要求模型同时满足主题相关性与细粒度指令约束的新任务。我们识别出IFTR中的两大核心挑战：（i）对内容范围（如包含与排除约束）的敏感性，以及（ii）对基于模式的约束（包括列语义与表征粒度）的感知能力——现有检索器大多缺乏这些能力。为支持系统性评估，我们引入了FollowTable——首个面向IFTR的大规模基准数据集，该数据集通过基于分类法的标注流程构建而成。我们进一步提出了一种新指标，称为指令响应度评分，用于评估检索排名是否能在仅基于主题的基线之上，一致地适应于用户指令。实验结果表明，现有检索模型难以在表格数据上遵循细粒度指令。具体而言，它们对表层语义线索存在系统性偏向，且在应对基于模式的约束时能力有限，这充分表明未来仍有显著的改进空间。