QUIETT: Query-Independent Table Transformation for Robust Reasoning
This addresses the problem of robust table reasoning for AI systems by decoupling table transformation from querying, offering a generalizable solution rather than an incremental improvement.
The paper tackles the problem of irregular schemas and heterogeneous formats in real-world tables that degrade table reasoning reliability by introducing QuIeTT, a query-independent framework that preprocesses tables into a canonical SQL-ready representation, resulting in consistent gains across four benchmarks with strong improvements on structurally diverse, unseen questions.
Real-world tables often exhibit irregular schemas, heterogeneous value formats, and implicit relational structure, which degrade the reliability of downstream table reasoning and question answering. Most existing approaches address these issues in a query-dependent manner, entangling table cleanup with reasoning and thus limiting generalization. We introduce QuIeTT, a query-independent table transformation framework that preprocesses raw tables into a single SQL-ready canonical representation before any test-time queries are observed. QuIeTT performs lossless schema and value normalization, exposes implicit relations, and preserves full provenance via raw table snapshots. By decoupling table transformation from reasoning, QuIeTT enables cleaner, more reliable, and highly efficient querying without modifying downstream models. Experiments on four benchmarks, WikiTQ, HiTab, NQ-Table, and SequentialQA show consistent gains across models and reasoning paradigms, with particularly strong improvements on a challenge set of structurally diverse, unseen questions.