What to Keep and What to Drop: Adaptive Table Filtering Framework
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
Abstract (arXiv)
Large language models (LLMs) for table-based reasoning often struggle with large tables due to input length limits. We propose ATF (Adaptive Table Filtering Framework), a modular and question-aware filtering pipeline that prunes uninformative columns and rows using LLM-generated column descriptions, clustering, and sparse-dense alignment scores. ATF integrates seamlessly with existing models (e.g., TAPAS, TAPEX) without retraining. Experiments show that ATF reduces table cells by 70%, boosting performance on out-of-domain TableQA tasks while causing slight performance drops on Table Fact Verification, where full-table context is more critical. These results highlight ATF's ability to adaptively balance informativeness and minimalism across tasks. Our code available at: https://github.com/torijune/ATF-Adaptive-Table-Filtering-Framework
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 19 of its 31 references that are in the map. First posted on arXiv 30 Jun 2025.
Builds on
19 of 31 references.
Closest ancestors · 3
Same direction, newest first
- 2024ALTER: Augmentation for Large-Table-Based Reasoning3 Jul
- 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding9 Jan
- 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning31 Jan
Other references · 16
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
- 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 515
- 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 434
- 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
- 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 408
- 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 235
- 2022Binding Language Models in Symbolic Languagescited by 228
- 2021AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industrycited by 44
4 more
- 2024TableRAG: Million-Token Table Understanding with Language Modelscited by 41
- 2023Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Tablecited by 40
- 2021SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)cited by 35
- 2024LLM Chain Ensembles for Scalable and Accurate Data Annotationcited by 11
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Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2023TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
- 2025TableReasoner: Advancing Table Reasoning Framework with Large Language Models
- 2025When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables
- 2026PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding
- 2024TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning
- 2025TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
- 2022Large Language Models are few(1)-shot Table Reasoners
- 2026Beyond Linearization: Attributed Table Graphs for Table Reasoning