CiteME: Can Language Models Accurately Cite Scientific Claims?
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arXiv
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| Format: | Preprint |
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2024
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| author | Press, Ori Hochlehnert, Andreas Prabhu, Ameya Udandarao, Vishaal Press, Ofir Bethge, Matthias |
| author_facet | Press, Ori Hochlehnert, Andreas Prabhu, Ameya Udandarao, Vishaal Press, Ofir Bethge, Matthias |
| contents | Thousands of new scientific papers are published each month. Such information overload complicates researcher efforts to stay current with the state-of-the-art as well as to verify and correctly attribute claims. We pose the following research question: Given a text excerpt referencing a paper, could an LM act as a research assistant to correctly identify the referenced paper? We advance efforts to answer this question by building a benchmark that evaluates the abilities of LMs in citation attribution. Our benchmark, CiteME, consists of text excerpts from recent machine learning papers, each referencing a single other paper. CiteME use reveals a large gap between frontier LMs and human performance, with LMs achieving only 4.2-18.5% accuracy and humans 69.7%. We close this gap by introducing CiteAgent, an autonomous system built on the GPT-4o LM that can also search and read papers, which achieves an accuracy of 35.3\% on CiteME. Overall, CiteME serves as a challenging testbed for open-ended claim attribution, driving the research community towards a future where any claim made by an LM can be automatically verified and discarded if found to be incorrect. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CiteME: Can Language Models Accurately Cite Scientific Claims? Press, Ori Hochlehnert, Andreas Prabhu, Ameya Udandarao, Vishaal Press, Ofir Bethge, Matthias Computation and Language Artificial Intelligence Human-Computer Interaction Thousands of new scientific papers are published each month. Such information overload complicates researcher efforts to stay current with the state-of-the-art as well as to verify and correctly attribute claims. We pose the following research question: Given a text excerpt referencing a paper, could an LM act as a research assistant to correctly identify the referenced paper? We advance efforts to answer this question by building a benchmark that evaluates the abilities of LMs in citation attribution. Our benchmark, CiteME, consists of text excerpts from recent machine learning papers, each referencing a single other paper. CiteME use reveals a large gap between frontier LMs and human performance, with LMs achieving only 4.2-18.5% accuracy and humans 69.7%. We close this gap by introducing CiteAgent, an autonomous system built on the GPT-4o LM that can also search and read papers, which achieves an accuracy of 35.3\% on CiteME. Overall, CiteME serves as a challenging testbed for open-ended claim attribution, driving the research community towards a future where any claim made by an LM can be automatically verified and discarded if found to be incorrect. |
| title | CiteME: Can Language Models Accurately Cite Scientific Claims? |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.12861 |