mlsanityData to 14 Oct 2026

Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

arXiv 2411.08516 ↗First posted 13 Nov 2024cs.CLcited by 20

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Abstract (arXiv)

The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Despite the impressive capabilities of large language models (LLMs) in advancing the natural language understanding frontier, their application to large-scale tabular data presents significant challenges, specifically regarding table size and complex intricate relationships. Existing works have shown promise with small-scale tables but often flounder when tasked with the complex reasoning required by larger, interconnected tables found in real-world scenarios. To address this gap, we introduce "Tree-of-Table", a novel approach designed to enhance LLMs' reasoning capabilities over large and complex tables. Our method employs Table Condensation and Decomposition to distill and reorganize relevant data into a manageable format, followed by the construction of a hierarchical Table-Tree that facilitates tree-structured reasoning. Through a meticulous Table-Tree Execution process, we systematically unravel the tree-structured reasoning chain to derive the solutions. Experiments across diverse datasets, including WikiTQ, TableFact, FeTaQA, and BIRD, demonstrate that Tree-of-Table sets a new benchmark with superior performance, showcasing remarkable efficiency and generalization capabilities in large-scale table reasoning.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 56 of its 71 references that are in the map. First posted on arXiv 13 Nov 2024.

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56 of 71 references.

References · 56

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 11; 2026: 7.

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  1. 2026Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddingscited by 24
  2. 2025TableMaster: A Recipe to Advance Table Understanding with Language Modelscited by 18
  3. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  4. 2025Retrv-R1: A Reasoning-Driven MLLM Framework for Universal and Efficient Multimodal Retrievalcited by 12
  5. 2025Structural and Statistical Texture Knowledge Distillation and Learning for Segmentationcited by 9
  6. 2025Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretrainingcited by 7
  7. 2025Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Modelscited by 7
  8. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 6
  9. 2025Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaborationcited by 6
  10. 2025Improved LLM Agents for Financial Document Question Answeringcited by 5
  11. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 5
  12. 2025RAVEN: Robust Advertisement Video Violation Temporal Grounding via Reinforcement Reasoningcited by 5
8 more
  1. 2025RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoningcited by 4
  2. 2024Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answeringcited by 3
  3. 2026V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimizationcited by 1
  4. 2026Position: How can Graphs Help Large Language Models?cited by 1
  5. 2026A Closer Look into LLMs for Table Understandingcited by 0
  6. 2026ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiringcited by 0
  7. 2026Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Modelscited by 0
  8. 2026CRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verificationcited by 0

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  3. 2025RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis
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