Abstract:Real-world table question answering often involves hybrid schemas in which some query-relevant information is explicit in relational columns, while other attributes, predicates, or join conditions are only implicit in free-form text. Existing systems struggle with this setting: Text-to-SQL methods scale to large and multi-table databases but require fully structured schemas, whereas direct LLM-based methods can interpret textual content but are costly and unreliable when applied to large databases. We present OmniTQA, a unified framework for semi-structured table question answering that treats semantic reasoning as a first-class operation within relational query execution. OmniTQA compiles natural-language questions into directed acyclic graphs of relational and LLM-based semantic operators. This enables ambiguity-aware plan diversification, cost-aware optimization, and dual-engine execution over structured and textual data. Across structured and semi-structured benchmarks, OmniTQA consistently improves performance in hybrid settings, outperforming the strongest baselines by 14 accuracy points on average and by 27 points on the most challenging subset, while maintaining competitive accuracy on fully structured datasets.
From: Nima Shahbazi [view email]
[v1]
Thu, 2 Apr 2026 18:16:11 UTC (3,334 KB)
[v2]
Wed, 1 Jul 2026 22:18:37 UTC (7,060 KB)
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