TopBench: A Benchmark for Implicit Predictive Reasoning in Tabular Question Answering
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, an...
| Authors | An-Yang Ji, Jun-Peng Jiang, De-Chuan Zhan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 April 2026 |
| Last updated at source | 17 June 2026 |
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