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ARXIV:2604.28076 · TABLE QA · SUBMITTED 01 MAY · 15:05 UTC · FRESHNESS STALE
ARXIV:2604.28076TABLE QASUBMITTED 01 MAY · 15:05 UTCFRESHNESS STALEAn-Yang Ji · Jun-Peng Jiang · De-Chuan Zhan · Han-Jia Ye · arXiv
TopBench: A new benchmark for evaluating LLMs on implicit prediction and reasoning over tabular data.
Opportunity summary
Pain TopBench: A new benchmark for evaluating LLMs on implicit prediction and reasoning over tabular data.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
TopBench: A new benchmark for evaluating LLMs on implicit prediction and reasoning over tabular data. However, a common class of real-world queries is implicitly predictive, requiring the inference of unobserved answers from historical patterns…
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…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Furthermore, elevating the upper bound of prediction precision requires the integration of more sophisticated modeling or reasoning capabilities. Code availability is flagged in the…
Table QA moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
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TopBench: A new benchmark for evaluating LLMs on implicit prediction and reasoning over tabular data.
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Paper Pack
10.48550/arXiv.2604.28076TopBench: A new benchmark for evaluating LLMs on implicit prediction and reasoning over tabular data.
Abstract
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.
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unverified0 refs; 3 sources; 50% coverage.
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PROBLEM
TopBench: A new benchmark for evaluating LLMs on implicit prediction and reasoning over tabular data. However, a common class of real-world queries is implicitly predictive, requiring the inference of unobserved answers from historical patterns rather than mere retrieval.
METHOD
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 his...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Furthermore, elevating the upper bound of prediction precision requires the integration of more sophisticated modeling or reasoning capabilities. Code availability is flagged in the production record; the...
WHY NOW
Table QA moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 35, "author": "An-Yang Ji; Jun-Peng Jiang; De-Chuan Zhan; Han-Jia Ye", "title": "TopBench: A Benchmark for Implicit Prediction and Reasoning over Tabular Question Answering"
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TopBench: A new benchmark for evaluating LLMs on implicit prediction and reasoning over tabular data.
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Table QA
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