Pub-Guard-LLM: Detecting Retracted Biomedical Articles with Reliable Explanations
This addresses the threat to research credibility in medicine by offering an open-source tool for fraud detection, though it is incremental as it applies existing LLM techniques to a specific domain.
The authors tackled the problem of detecting fraudulent biomedical articles by proposing Pub-Guard-LLM, a large language model-based system, and showed that it consistently surpasses baselines in performance and provides more reliable explanations on a benchmark of over 11K articles.
A significant and growing number of published scientific articles is found to involve fraudulent practices, posing a serious threat to the credibility and safety of research in fields such as medicine. We propose Pub-Guard-LLM, the first large language model-based system tailored to fraud detection of biomedical scientific articles. We provide three application modes for deploying Pub-Guard-LLM: vanilla reasoning, retrieval-augmented generation, and multi-agent debate. Each mode allows for textual explanations of predictions. To assess the performance of our system, we introduce an open-source benchmark, PubMed Retraction, comprising over 11K real-world biomedical articles, including metadata and retraction labels. We show that, across all modes, Pub-Guard-LLM consistently surpasses the performance of various baselines and provides more reliable explanations, namely explanations which are deemed more relevant and coherent than those generated by the baselines when evaluated by multiple assessment methods. By enhancing both detection performance and explainability in scientific fraud detection, Pub-Guard-LLM contributes to safeguarding research integrity with a novel, effective, open-source tool.