Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data

• URL: http://arxiv.org/abs/2507.00152v1
• Date: Mon, 30 Jun 2025 18:04:36 GMT
• Title: Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data
• Authors: Ekaterina Borisova, Fabio Barth, Nils Feldhus, Raia Abu Ahmad, Malte Ostendorff, Pedro Ortiz Suarez, Georg Rehm, Sebastian Möller,
• Abstract summary: We investigate the effectiveness of both text-based and multimodal LLMs on table understanding tasks.<n>We compare their performance on tables from scientific vs. non-scientific contexts and examine their robustness on tables represented as images vs. text.
• Score: 12.56716294438794
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tables are among the most widely used tools for representing structured data in research, business, medicine, and education. Although LLMs demonstrate strong performance in downstream tasks, their efficiency in processing tabular data remains underexplored. In this paper, we investigate the effectiveness of both text-based and multimodal LLMs on table understanding tasks through a cross-domain and cross-modality evaluation. Specifically, we compare their performance on tables from scientific vs. non-scientific contexts and examine their robustness on tables represented as images vs. text. Additionally, we conduct an interpretability analysis to measure context usage and input relevance. We also introduce the TableEval benchmark, comprising 3017 tables from scholarly publications, Wikipedia, and financial reports, where each table is provided in five different formats: Image, Dictionary, HTML, XML, and LaTeX. Our findings indicate that while LLMs maintain robustness across table modalities, they face significant challenges when processing scientific tables.

Related papers

• TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models [30.26407735827857]
  Reasoning with table-structured data poses significant challenges for large language models (LLMs)<n>We present a comprehensive table reasoning evolution benchmark, TReB, which measures both shallow table understanding abilities and deep table reasoning abilities.<n>We create an evaluation framework to robustly measure table reasoning capabilities with three distinct inference modes, TCoT, PoT and ICoT.
  arXiv  Detail & Related papers  (2025-06-23T09:02:04Z)
• MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark [70.47478110973042]
  We introduce MMTU, a large-scale benchmark with over 30K questions across 25 real-world table tasks.<n> MMTU is designed to comprehensively evaluate models ability to understand, reason, and manipulate real tables at the expert-level.<n>We show that MMTU require a combination of skills -- including table understanding, reasoning, and coding -- that remain challenging for today's frontier models.
  arXiv  Detail & Related papers  (2025-06-05T21:05:03Z)
• Better Think with Tables: Tabular Structures Enhance LLM Comprehension for Data-Analytics Requests [33.471112091886894]
  Large Language Models (LLMs) often struggle with data-analytics requests related to information retrieval and data manipulation.<n>We introduce Thinking with Tables, where we inject tabular structures into LLMs for data-analytics requests.<n>We show that providing tables yields a 40.29 percent average performance gain along with better manipulation and token efficiency.
  arXiv  Detail & Related papers  (2024-12-22T23:31:03Z)
• Benchmarking Table Comprehension In The Wild [9.224698222634789]
  TableQuest is a new benchmark designed to evaluate the holistic table comprehension capabilities of Large Language Models (LLMs)<n>We experiment with 7 state-of-the-art models, and find that despite reasonable accuracy in locating facts, they often falter when required to execute more sophisticated reasoning or multi-step calculations.
  arXiv  Detail & Related papers  (2024-12-13T05:52:37Z)
• SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction [1.0624606551524207]
  Existing datasets often focus on scientific tables due to the vast amount of academic articles.<n>Current datasets often lack the words, and their positions, contained within the tables.<n>We present SynFinTabs, a large-scale, labelled dataset of synthetic financial tables.
  arXiv  Detail & Related papers  (2024-12-05T15:42:59Z)
• TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios [51.66718740300016]
  TableLLM is a robust large language model (LLM) with 8 billion parameters.<n>TableLLM is purpose-built for proficiently handling data manipulation tasks.<n>We have released the model checkpoint, source code, benchmarks, and a web application for user interaction.
  arXiv  Detail & Related papers  (2024-03-28T11:21:12Z)
• Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering [53.56653281752486]
  This study explores Large Language Models' mathematical reasoning on four financial question-answering datasets. We focus on sensitivity to table complexity and performance variations with an increasing number of arithmetic reasoning steps. We introduce a novel prompting technique tailored to semi-structured documents, matching or outperforming other baselines in performance.
  arXiv  Detail & Related papers  (2024-02-17T05:10:18Z)
• Large Language Model for Table Processing: A Survey [18.32332372134988]
  This survey provides a comprehensive overview of table-related tasks. It covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data analysis.
  arXiv  Detail & Related papers  (2024-02-04T00:47:53Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• PixT3: Pixel-based Table-To-Text Generation [66.96636025277536]
  We present PixT3, a multimodal table-to-text model that overcomes the challenges of linearization and input size limitations. Experiments on the ToTTo and Logic2Text benchmarks show that PixT3 is competitive and superior to generators that operate solely on text.
  arXiv  Detail & Related papers  (2023-11-16T11:32:47Z)
• A Graph Representation of Semi-structured Data for Web Question Answering [96.46484690047491]
  We propose a novel graph representation of Web tables and lists based on a systematic categorization of the components in semi-structured data as well as their relations. Our method improves F1 score by 3.90 points over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-10-14T04:01:54Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.