This Reference Does Not Exist: An Exploration of LLM Citation Accuracy and Relevance

Courtni Byun, Piper Vasicek, Kevin Seppi

Abstract
Citations are a fundamental and indispensable part of research writing. They provide support and lend credibility to research findings. Recent GPT-fueled interest in large language models (LLMs) has shone a spotlight on the capabilities and limitations of these models when generating relevant citations for a document. Recent work has focused largely on title and author accuracy. We underline this effort and expand on it with a preliminary exploration in relevance of model-recommended citations. We define three citation-recommendation tasks. We also collect and annotate a dataset of model-recommended citations for those tasks. We find that GPT-4 largely outperforms earlier models on both author and title accuracy in two markedly different CS venues, but may not recommend references that are more relevant than those recommended by the earlier models. The two venues we compare are CHI and EMNLP. All models appear to perform better at recommending EMNLP papers than CHI papers.
Anthology ID:
2024.hcinlp-1.3
Volume:
Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Su Lin Blodgett, Amanda Cercas Curry, Sunipa Dey, Michael Madaio, Ani Nenkova, Diyi Yang, Ziang Xiao
Venues:
HCINLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–39
Language:
URL:
https://aclanthology.org/2024.hcinlp-1.3
DOI:
Bibkey:
Cite (ACL):
Courtni Byun, Piper Vasicek, and Kevin Seppi. 2024. This Reference Does Not Exist: An Exploration of LLM Citation Accuracy and Relevance. In Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing, pages 28–39, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
This Reference Does Not Exist: An Exploration of LLM Citation Accuracy and Relevance (Byun et al., HCINLP-WS 2024)
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