Analysis for Question Answering over Figures and Tables in Agriculture and Forestry Documents

Abstract

This study examines the ability of large language models (LLMs) to interpret complex, machine-unreadable tables found in agricultural and forestry guidelines. Analysis of QA outputs revealed that increasing table complexity contributes to reduced performance, particularly for tables requiring temporal or intuitive understanding. Prompt design was also found to significantly affect output accuracy