Tables as Images? Exploring the Strengths and Limitations of LLMs on Multimodal Representations of Tabular Data
CoRR(2024)
摘要
In this paper, we investigate the effectiveness of various LLMs in
interpreting tabular data through different prompting strategies and data
formats. Our analysis extends 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 influence of representation and prompting on
LLM performance. Our study provides insights into the effective use of LLMs on
table-related tasks.
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