mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
LLM-Powered Semi-Structured Table Question Answering
Curated list of awesome datasets for various table understanding tasks
Rule-based spreadsheet data extraction and transformation
Code for the paper "Towards Explainable Table Interpretation Using Multi-view Explanations". ICDE 2023.
TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding. ICLR 2026.
ChemTable is a large-scale benchmark designed to test the capabilities of multimodal large language models (MLLMs) in understanding real-world chemical tables—one of the most information-dense and visually complex formats in scientific literature.
[EMNLP 2021] Table-based Fact Verification with Salience-aware Learning.
Curated list of awesome paper for table understanding tasks
Table-Text Alignment: Explaining Claim Verification
Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data
(Findings of ACL 2025) TabXEval: an exhaustive, explainable rubric + two-phase framework (TabAlign → TabCompare) for table evaluation with TabXBench.
End-to-end evaluation of retrieval-augmented generation on reference tables. Per-page ColPali image embedding with multimodal chain-of-thought lifts table-question correctness from 48.9% to 84.4%.
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