Hallucinations in Bibliographic Recommendation: Citation Frequency as a Proxy for Training Data Redundancy
NeutralArtificial Intelligence
A recent study published on arXiv explores the challenges of hallucinations in bibliographic recommendations made by large language models (LLMs). It suggests that the frequency of citations could serve as a useful proxy for understanding the redundancy in training data. This research is significant as it addresses a critical issue in the application of LLMs, particularly in ensuring the accuracy of bibliographic information, which is essential for researchers and academics relying on these tools.
— Curated by the World Pulse Now AI Editorial System

