OSX at Context24: How Well Can GPT Tackle Contexualizing Scientific Figures and Tables

AbstractIdentifying the alignment between different parts of a scientific paper is fundamental to scholarly document processing.In the Context24 shared task, participants are given a scientific claim and asked to identify (1) key figures or tables that support the claim and (2) methodological details.While employing a supervised approach to train models on task-specific data is a prevailing strategy for both subtasks, such an approach is not feasible for low-resource domains.Therefore, this paper introduces data-free systems supported by Large Language Models.We propose systems based on GPT-4o and GPT-4-turbo for each task.The experimental results reveal the zero-shot capabilities of GPT-4* in both tasks.