Tag
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.