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该论文提出 LTEM（LLM-Friendly Table-Enhanced Markdown），一种在 Markdown 基础上最小扩展以显式编码合并单元格与多级表头的格式，并构建了表结构复杂度分类体系（F1-F6）和受控基准 STB（1,089 张表）。实验表明 LTEM 在表格理解任务上达到与 HTML 相当的准确率，同时在 token 成本与响应稳定性上优于 HTML/LaTeX。
EdgeLM is a research paper proposing a retrieval framework that selects edge demonstrations—relevant examples near decision boundaries—to improve LLM performance on table understannding and data wrangling tasks.
This paper introduces CoCoEvolve, a self-supervised method that improves cross-representation understanding across charts, tables, and code by enforcing one-to-one consistency between representations, with training-time and test-time co-evolution objectives.
Presents TELLER, a dual-path iterative preference optimization approach for table entity linking, with direct-answer and reasoning paths that improve accuracy on TableInstruct and MammoTab V2 benchmarks.
HakushoBench is a Japanese chart and table VQA benchmark built from governmental white papers to evaluate vision-language models' understanding of complex visual data, challenging open-weight models with a 58.6% accuracy and a 34.9-point gap to proprietary models.
WildTableBench introduces the first question-answering benchmark for real-world table images, revealing that existing multimodal foundation models struggle significantly with structural perception and numerical reasoning, with only one model exceeding 50% accuracy.
TabularMath introduces a benchmark and AutoT2T framework for evaluating LLMs' mathematical reasoning over tabular data, revealing that table complexity, data quality, and modality significantly impact model performance. The study addresses a gap in LLM evaluation by systematically assessing robustness to incomplete or inconsistent table information in real-world scenarios.