2024/11/27 by Deepak Gupta, Dina Demner-Fushman, Gupta, Deepak +8 · 1 voice · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Biomedical Text Mining and Ontologies #Computer science #Generative grammar #Information retrieval #Natural language processing #Track (disk drive) #World Wide Web #cs.IR
paper · pdf · doi:10.48550/arxiv.2411.18069
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
With the advancement of large language models (LLMs), the biomedical domain has seen significant progress and improvement in multiple tasks such as biomedical question answering, lay language summarization of the biomedical literature, clinical note summarization, etc. However, hallucinations or confabulations remain one of the key challenges when using LLMs in the biomedical and other domains. Inaccuracies may be particularly harmful in high-risk situations, such as medical question answering, making clinical decisions, or appraising biomedical research. Studies on the evaluation of the LLMs abilities to ground generated statements in verifiable sources have shown that models perform significantly worse on lay-user-generated questions, and often fail to reference relevant sources. This can be problematic when those seeking information want evidence from studies to back up the claims from LLMs. Unsupported statements are a major barrier to using LLMs in any applications that may affect health. Methods for grounding generated statements in reliable sources along with practical evaluation approaches are needed to overcome this barrier. Towards this, in our pilot task organized at TREC 2024, we introduced the task of reference attribution as a means to mitigate the generation of false statements by LLMs answering biomedical questions.