Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregation. However, a common class of real-world queries is implicitly predictive, requiring the inference of unobserved answers from historical patterns rather than mere retrieval. These queries introduce two challenges: recognizing latent intent and reliable predictive reasoning over massive tables. To assess LLMs in such Tabular questiOn answering with implicit Prediction tasks, we introduce TopBench, a benchmark consisting of 779 samples across four sub-tasks, ranging from single-point prediction to decision making, treatment effect analysis, and complex filtering, requiring models to generate outputs spanning reasoning text and structured tables. We evaluate diverse models under both text-based and agentic workflows. Experiments reveal that current models often struggle with intent recognition, defaulting to just lookups. Deeper analysis identifies that accurate intent disambiguation serves as the prerequisite for leading these predictive behaviors. Furthermore, elevating the upper bound of prediction precision requires the integration of more sophisticated modeling or reasoning capabilities.

翻译：大型语言模型（LLMs）推进了表格问答的发展，其中大多数查询可通过信息提取或简单聚合来回答。然而，现实世界中一类常见查询具有隐式预测性，需要从历史模式推断未观测到的答案，而非单纯检索。这类查询带来两大挑战：识别潜在意图以及对大规模表格进行可靠的预测性推理。为评估LLMs在隐式预测任务中的表格问答能力，我们提出TopBench基准测试，该基准包含779个样本，涵盖从单点预测到决策制定、治疗效应分析及复杂筛选四个子任务，要求模型生成涵盖推理文本和结构化表格的输出。我们评估了多种模型在基于文本和基于智能体工作流下的表现。实验表明，当前模型通常难以识别意图，默认仅执行查询操作。深入分析发现，准确的意图消歧是引导这些预测行为的先决条件。此外，提升预测精度的上限需要整合更复杂的建模或推理能力。