Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation

• URL: http://arxiv.org/abs/2510.09671v1
• Date: Wed, 08 Oct 2025 14:56:03 GMT
• Title: Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation
• Authors: Wei Zhou, Bolei Ma, Annemarie Friedrich, Mohsen Mesgar,
• Abstract summary: The Table Question Answering (TQA) task spans diverse settings, varying in table representation, question/answer complexity, modality involved, and domain.<n>Recent advances in large language models (LLMs) have led to substantial progress in TQA, but the field still lacks a systematic organization and understanding of task formulations, core challenges, and methodological trends.<n>This survey addresses this gap by providing a comprehensive and structured overview of TQA research with a focus on LLM-based methods.
• Score: 18.503489117909503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Table Question Answering (TQA) aims to answer natural language questions about tabular data, often accompanied by additional contexts such as text passages. The task spans diverse settings, varying in table representation, question/answer complexity, modality involved, and domain. While recent advances in large language models (LLMs) have led to substantial progress in TQA, the field still lacks a systematic organization and understanding of task formulations, core challenges, and methodological trends, particularly in light of emerging research directions such as reinforcement learning. This survey addresses this gap by providing a comprehensive and structured overview of TQA research with a focus on LLM-based methods. We provide a comprehensive categorization of existing benchmarks and task setups. We group current modeling strategies according to the challenges they target, and analyze their strengths and limitations. Furthermore, we highlight underexplored but timely topics that have not been systematically covered in prior research. By unifying disparate research threads and identifying open problems, our survey offers a consolidated foundation for the TQA community, enabling a deeper understanding of the state of the art and guiding future developments in this rapidly evolving area.

Related papers

• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• Who Gets Cited Most? Benchmarking Long-Context Language Models on Scientific Articles [81.89404347890662]
  SciTrek is a novel question-answering benchmark designed to evaluate the long-context reasoning capabilities of large language models (LLMs) using scientific articles.<n>Our analysis reveals systematic shortcomings in models' abilities to perform basic numerical operations and accurately locate specific information in long contexts.
  arXiv  Detail & Related papers  (2025-09-25T11:36:09Z)
• Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey [54.90240495777929]
  Ambiguity remains a fundamental challenge in Natural Language Processing (NLP)<n>With the advent of Large Language Models (LLMs), addressing ambiguity has become even more critical due to their expanded capabilities and applications.<n>This paper explores the definition, forms, and implications of ambiguity for language driven systems.
  arXiv  Detail & Related papers  (2025-05-18T20:53:41Z)
• A Systematic Survey on Instructional Text: From Representation Formats to Downstream NLP Tasks [0.0]
  Real-world tasks often involve complex, multi-step instructions that remain challenging for current NLP systems. Our study examines 177 papers, identifying trends, challenges, and opportunities in this emerging field.
  arXiv  Detail & Related papers  (2024-10-24T08:22:59Z)
• From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
  Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making.<n>Large foundation models, such as large language models, have revolutionized various natural language processing tasks.<n>This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
  arXiv  Detail & Related papers  (2024-03-18T17:57:09Z)
• Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey [17.19337964440007]
  There is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This survey aims to address this gap by consolidating recent progress in these areas, offering a thorough survey and taxonomy of the datasets, metrics, and methodologies utilized. It identifies strengths, limitations, unexplored territories, and gaps in the existing literature, while providing some insights for future research directions in this vital and rapidly evolving field.
  arXiv  Detail & Related papers  (2024-02-27T23:59:01Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Thread of Thought Unraveling Chaotic Contexts [133.24935874034782]
  "Thread of Thought" (ThoT) strategy draws inspiration from human cognitive processes. In experiments, ThoT significantly improves reasoning performance compared to other prompting techniques.
  arXiv  Detail & Related papers  (2023-11-15T06:54:44Z)
• Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications [41.24492058141363]
  Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. We propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
  arXiv  Detail & Related papers  (2023-11-10T05:24:04Z)
• Re-Reading Improves Reasoning in Large Language Models [87.46256176508376]
  We introduce a simple, yet general and effective prompting method, Re2, to enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs) Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. We evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.
  arXiv  Detail & Related papers  (2023-09-12T14:36:23Z)
• Towards Complex Document Understanding By Discrete Reasoning [77.91722463958743]
  Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language. We introduce a new Document VQA dataset, named TAT-DQA, which consists of 3,067 document pages and 16,558 question-answer pairs. We develop a novel model named MHST that takes into account the information in multi-modalities, including text, layout and visual image, to intelligently address different types of questions.
  arXiv  Detail & Related papers  (2022-07-25T01:43:19Z)
• Conversational Question Answering: A Survey [18.447856993867788]
  This survey is an effort to present a comprehensive review of the state-of-the-art research trends of Conversational Question Answering (CQA) Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives.
  arXiv  Detail & Related papers  (2021-06-02T01:06:34Z)
• A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges [71.4531144086568]
  Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions. Researchers have shifted their attention from simple questions to complex questions, which require more KB triples and constraint inference.
  arXiv  Detail & Related papers  (2020-07-26T07:13:32Z)

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.