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AI Research
arXiv CS.AI·May 28·5 min read
CiteCheck: Retrieval-Grounded Detection of LLM Citation Hallucinations in Scientific Text
TL;DR
Researchers identified a significant issue where large language models (LLMs) generate false citations, known as citation hallucinations, in scientific texts. They developed CiteCheck, a tool that uses retrieval-augmented generation to verify citations against a database of scientific literature.
✦ Why It Matters
Engineers and researchers can integrate CiteCheck to enhance the reliability of AI-generated scientific documents.
Key Takeaways
- Implement CiteCheck to verify citations in AI-generated texts.
- Train models on curated datasets to improve citation accuracy.
- Utilize retrieval-augmented generation for better information retrieval.
- Monitor citation integrity to enhance scientific credibility.
Full Summary
Citation hallucinations occur when large language models (LLMs) fabricate references that do not exist, undermining the credibility of scientific writing. CiteCheck was created to address this issue by employing a retrieval-augmented generation approach, which cross-references generated citations with a curated database of scientific articles.
The methodology involved training the tool on a dataset of scientific texts and their corresponding citations, allowing it to learn patterns of accurate citation. Results showed that CiteCheck significantly reduced the rate of false citations, achieving an accuracy improvement of over 30%.
This advancement not only enhances the reliability of AI-generated scientific content but also provides a framework for future tools aimed at improving citation integrity. The implications for engineers and researchers include the potential for integrating CiteCheck into existing workflows to ensure the accuracy of references in AI-generated texts.