Summary of Citeme: Can Language Models Accurately Cite Scientific Claims?, by Ori Press et al.
CiteME: Can Language Models Accurately Cite Scientific Claims?
by Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, Matthias Bethge
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers investigate whether language models (LMs) can serve as research assistants to correctly identify referenced papers. They create a benchmark, CiteME, comprising text excerpts from recent machine learning papers referencing single other papers. The results show a significant gap between LM performance and human accuracy, with LMs achieving 4.2-18.5% accuracy compared to humans’ 69.7%. To bridge this gap, the authors introduce CiteAgent, an autonomous system built on GPT-4o LM that can search, read, and identify referenced papers with 35.3% accuracy on CiteME. This benchmark drives the research community towards a future where LMs’ claims can be verified and discarded if found incorrect. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to figure out if language models (LMs) can help find the correct paper when given a short summary of another paper. They made a test set, called CiteME, with short summaries from recent machine learning papers that reference one other paper each. The results show that LMs aren’t very good at this yet, but they’re getting better! To make it even better, the researchers created a new tool, CiteAgent, that can search for and read papers too. This will help us in the future by making sure what language models say is true or not. |
Keywords
» Artificial intelligence » Gpt » Machine learning
