arXiv AI
Jul 29
TabRank: Chain-of-Thought Distillation for Table Re-Rankers
arXiv:2607. 25182v1 Announce Type: cross Abstract: The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval.
Beyond Linearization: Attributed Table Graphs for Table Reasoning
The paper introduces Table Graph Reasoner (TabGR), a model that represents tables as an Attributed Table Graph (ATG) to preserve row-column-cell structure and enable graph-based reasoning without task-specific training. It also proposes a Question-Guided Personalized PageRank (QG-PPR) mechanism to rerank tabular data and address the lost-in-the-middle issue. Experiments on multiple table reasoning benchmarks show that TabGR outperforms state-of-the-art models by up to 9.7% in accuracy.
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arXiv AI
Aug 26
PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding
PARTAB is a framework that improves large language model reasoning on tables by constructing a structured evidence interface. It represents query‑relevant evidence as semantically coherent, row‑linked table regions and performs hierarchical selection over column groups and row‑level partitions before composing the evidence for answer generation. Evaluations on multiple table reasoning benchmarks show that PARTAB consistently outperforms full‑table prompting and recent methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical reasoning tasks.
arXiv AI
Jul 28
Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering
arXiv:2607. 22633v1 Announce Type: new Abstract: Table Question Answering (TableQA) aims to reason over tables to answer user queries.
Hugging Face Trending Papers
Aug 25
PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding
Large Language Models (LLMs) have shown strong capabilities in table reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and difficulty localizi...
Hugging Face Trending Papers
Sep 1
H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning
Tables are ubiquitous across diverse domains, yet reasoning over them remains a significant challenge for modern large language models (LLMs). Current approaches typically linearize tables into sequen...
arXiv AI
Sep 2
H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning
H2Table introduces a hierarchical hypergraph representation for complex tables, enabling a hypergraph encoder to capture semantic relationships between headers and cells. The framework uses learnable query vectors to extract structural embeddings for large language models. Experiments on the HiTab dataset show a 22.88% improvement over state‑of‑the‑art baselines on tables with four levels of nesting.
arXiv AI
Aug 18
Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
arXiv:2605. 20254v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, because Table Question-Answering (TQA) requires precise cell retrieval and multi-step structured reasoning.
arXiv AI
Jul 22
TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
arXiv:2506. 18421v3 Announce Type: replace-cross Abstract: The majority of data in businesses and industries is stored in tables, databases, and data warehouses.
arXiv AI
Aug 5
DocTrace: Towards Traceable Long Document VQA via Hierarchical Evidence Graph Reasoning
arXiv:2608. 03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneous document elements distributed across multiple pages.
Google AI Blog
Mar 11, 2024
Chain-of-table: Evolving tables in the reasoning chain for table understanding
Posted by Zilong Wang, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural language processing (NLP).
arXiv Machine Learning
Aug 3
TELLER: Dual-Path Iterative Preference Optimization for Table Entity Linking
arXiv:2607. 28680v1 Announce Type: cross Abstract: Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities.
arXiv AI
Sep 4
TabScope: Question-Adaptive Scope Selection for Table Question Answering
TabScope introduces a question‑adaptive framework for table question answering that dynamically chooses between localized and full‑table reasoning. It constructs question‑specific sub‑tables via operation‑aware decomposition and predicts the question type to select the appropriate reasoning mode. Experiments on WikiTQ and the new SLQA benchmark show that localization improves lookup and local reasoning questions, while adaptive selection yields the best overall performance on long tables.