Abstract
Identifying 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.- Anthology ID:
- 2024.sdp-1.31
- Volume:
- Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Tirthankar Ghosal, Amanpreet Singh, Anita Waard, Philipp Mayr, Aakanksha Naik, Orion Weller, Yoonjoo Lee, Shannon Shen, Yanxia Qin
- Venues:
- sdp | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 324–331
- Language:
- URL:
- https://aclanthology.org/2024.sdp-1.31
- DOI:
- Cite (ACL):
- Tosho Hirasawa. 2024. OSX at Context24: How Well Can GPT Tackle Contexualizing Scientific Figures and Tables. In Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), pages 324–331, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- OSX at Context24: How Well Can GPT Tackle Contexualizing Scientific Figures and Tables (Hirasawa, sdp-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.sdp-1.31.pdf