# When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables arXiv: https://arxiv.org/abs/2509.17680 ยท HTML: https://mlsanity.com/paper/2509.17680/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 52 references that are in the map. - First posted on arXiv 22 Sep 2025. ## Builds on (most cited first, 27 of 27) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022) - [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018) - [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015) - [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020) - [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019) - [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020) - [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021) - [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022) - [Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding](https://mlsanity.com/paper/2401.04398/) (2024) - [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023) - [FeTaQA: Free-form Table Question Answering](https://mlsanity.com/paper/2104.00369/) (2021) - [CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL](https://mlsanity.com/paper/2410.01943/) (2024) - [ReAcTable: Enhancing ReAct for Table Question Answering](https://mlsanity.com/paper/2310.00815/) (2023) - [PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training](https://mlsanity.com/paper/2211.02816/) (2022) - [TableRAG: Million-Token Table Understanding with Language Models](https://mlsanity.com/paper/2410.04739/) (2024) - [Table-R1: Region-based Reinforcement Learning for Table Understanding](https://mlsanity.com/paper/2505.12415/) (2025) - [Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning](https://mlsanity.com/paper/2502.11799/) (2025) - [Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing](https://mlsanity.com/paper/2206.14017/) (2022) - [Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs](https://mlsanity.com/paper/2509.10377/) (2025) - [ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context](https://mlsanity.com/paper/2403.02177/) (2024) - [H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables](https://mlsanity.com/paper/2407.05952/) (2024) - [NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization](https://mlsanity.com/paper/2406.17961/) (2024) - [Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering](https://mlsanity.com/paper/2412.20145/) (2024) - [Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering](https://mlsanity.com/paper/2410.12846/) (2024) - [Weaver: Interweaving SQL and LLM for Table Reasoning](https://mlsanity.com/paper/2505.18961/) (2025) ## Built on by (most cited first, 10 of 10) - [ExpSeek: Self-Triggered Experience Seeking for Web Agents](https://mlsanity.com/paper/2601.08605/) (2026) - [ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch](https://mlsanity.com/paper/2601.13606/) (2026) - [Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search](https://mlsanity.com/paper/2601.03851/) (2026) - [One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement](https://mlsanity.com/paper/2604.25444/) (2026) - [AWM: Answerable Working Memory for Long-Document VQA Agents](https://mlsanity.com/paper/2608.25618/) (2026) - [All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs](https://mlsanity.com/paper/2609.06161/) (2026) - [An Empirical Investigation of Robustness in Large Language Models under Tabular Distortions](https://mlsanity.com/paper/2601.05009/) (2026) - [Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective](https://mlsanity.com/paper/2605.29319/) (2026) - [CEDAR: Automata as Verifiable Interfaces for Language-Guided Embodied Action](https://mlsanity.com/paper/2608.27797/) (2026) - [TabScope: Question-Adaptive Scope Selection for Table Question Answering](https://mlsanity.com/paper/2609.03395/) (2026) ## Abstract (arXiv) Table question answering (TableQA) is a fundamental task in natural language processing (NLP). The strong reasoning capabilities of large language models (LLMs) have brought significant advances in this field. However, as real-world applications involve increasingly complex questions and larger tables, substantial noisy data is introduced, which severely degrades reasoning performance. To address this challenge, we focus on improving two core capabilities: Relevance Filtering, which identifies and retains information truly relevant to reasoning, and Table Pruning, which reduces table size while preserving essential content. Based on these principles, we propose EnoTab, a dual denoising framework for complex questions and large-scale tables. Specifically, we first perform Evidence-based Question Denoising by decomposing the question into minimal semantic units and filtering out those irrelevant to answer reasoning based on consistency and usability criteria. Then, we propose Evidence Tree-guided Table Denoising, which constructs an explicit and transparent table pruning path to remove irrelevant data step by step. At each pruning step, we observe the intermediate state of the table and apply a post-order node rollback mechanism to handle abnormal table states, ultimately producing a highly reliable sub-table for final answer reasoning. Finally, extensive experiments show that EnoTab achieves outstanding performance on TableQA tasks with complex questions and large-scale tables, confirming its effectiveness.