TY - GEN
T1 - The Search for Conflicts of Interest
T2 - 30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025
AU - Gaur, Garima
AU - Balalau, Oana
AU - Manolescu, Ioana
AU - Upadhyay, Prajna Devi
N1 - Publisher Copyright:
©2025 Association for Computational Linguistics.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - A conflict of interest (COI) appears when a person or a company has two or more interests that may directly conflict. This happens, for instance, when a scientist whose research is funded by a company audits the same company. For transparency and to avoid undue influence, public repositories of relations of interest are increasingly recommended or mandated in various domains, and can be used to avoid COIs. In this work, we propose an LLM-based open information extraction (OpenIE) framework for extracting financial or other types of interesting relations from scientific text. We target scientific publications in which authors declare funding sources or collaborations in the acknowledgment section, in the metadata, or in the publication, following editors’ requirements. We introduce an extraction methodology and present a knowledge base (KB) with a comprehensive taxonomy of COI centric relations. Finally, we perform a comparative study of disclosures of two journals in the field of toxicology and pharmacology.
AB - A conflict of interest (COI) appears when a person or a company has two or more interests that may directly conflict. This happens, for instance, when a scientist whose research is funded by a company audits the same company. For transparency and to avoid undue influence, public repositories of relations of interest are increasingly recommended or mandated in various domains, and can be used to avoid COIs. In this work, we propose an LLM-based open information extraction (OpenIE) framework for extracting financial or other types of interesting relations from scientific text. We target scientific publications in which authors declare funding sources or collaborations in the acknowledgment section, in the metadata, or in the publication, following editors’ requirements. We introduce an extraction methodology and present a knowledge base (KB) with a comprehensive taxonomy of COI centric relations. Finally, we perform a comparative study of disclosures of two journals in the field of toxicology and pharmacology.
UR - https://www.scopus.com/pages/publications/105028937301
U2 - 10.18653/v1/2025.findings-emnlp.748
DO - 10.18653/v1/2025.findings-emnlp.748
M3 - Conference contribution
AN - SCOPUS:105028937301
T3 - EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2025
SP - 13922
EP - 13936
BT - EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2025
A2 - Christodoulopoulos, Christos
A2 - Chakraborty, Tanmoy
A2 - Rose, Carolyn
A2 - Peng, Violet
PB - Association for Computational Linguistics (ACL)
Y2 - 4 November 2025 through 9 November 2025
ER -