Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
In this paper, we investigate the effectiveness of various LLMs in interpreting tabular data through different prompting strategies and data formats. Our analyses extend across six benchmarks for table-related tasks such as question-answering and fact-checking. We introduce for the first time the assessment of LLMs' performance on image-based table representations. Specifically, we compare five text-based and three image-based table representations, demonstrating the role of representation and prompting on LLM performance. Our study provides insights into the effective use of LLMs on table-related tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Fact CheckingQuestion AnsweringSimilar Papers 제목 키워드 기반
Logical Inference for Counting on Semi-structured Tables
Recently, the Natural Language Inference (NLI) task has been studied for semi-structured tables that do not have a strict format. Although neural approaches have achieved high performance in various types of NLI, includi…
Natural Language InferenceA Table Is Worth 64 Tokens: Pixel-level Compression for Multi-Table Document Question Answering
Answering questions over real-world documents requires processing long inputs that interleave text with tables. Optical context compression, which represents context as images, promises to reduce token cost, but its effe…
Question AnsweringTeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation
Large Language Models (LLMs) are increasingly applied to telecom engineering tasks, yet perform poorly on 3GPP specifications. These standards encode much of their technical information in complex tables, but LLM knowled…
TransientTables: Evaluating LLMs' Reasoning on Temporally Evolving Semi-structured Tables
Humans continuously make new discoveries, and understanding temporal sequence of events leading to these breakthroughs is essential for advancing science and society. This ability to reason over time allows us to identif…
Question GenerationQuestion-GenerationDocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert do…
Math