Budgeted Data Error Detection for Table Question Answering

Table question answering (TQA) can fail because of errors in the underlying data, yet data errors remains a largely overlooked source of TQA failures. Error detection is typically performed Offline, targeting all data in the database. However, when detection relies on costly LLMs, limited budgets make it important to allocate detection effort effectively. Queries provide signals for focusing this effort on data that can affect the answer, motivating Online, query-aware detection. We formulate budgeted error detection for TQA and introduce an Online detection policy that leverages the query to interleave detection with execution. We develop a data error taxonomy and propose BED-TQA, a configurable benchmark for budgeted error detection derived from four TQA datasets. We evaluate detection policies across budgets, error distribution, query scope, database size and workload conditions. Given the same budget to scan all columns once, TQA with Online detection achieves up to 33.2% higher answer accuracy than Offline detection, while using 59.2% fewer LLM tokens. Online detection remains competitive across most settings, particularly under tight budgets. Offline becomes more cost-effective only when repairs can be reused across multiple queries. Overall, our results challenge the popular assumption that data errors should be detected and repaired Offline: Online detection can allocate budget more effectively toward relevant data.