AI RESEARCH
Calibrated Confidence Estimation for Tabular Question Answering
arXiv CS.CL
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ArXi:2604.12491v1 Announce Type: new Large language models (LLMs) are increasingly deployed for tabular question answering, yet calibration on structured data is largely unstudied. This paper presents the first systematic comparison of five confidence estimation methods across five frontier LLMs and two tabular QA benchmarks. All models are severely overconfident (smooth ECE 0.35-0.64 versus 0.10-0.15 reported for textual QA