Yale NLP/LLM Interest Group Talks

In the biomedical domain, finding relevant information in vast scientific literature is challenging, making citation recommendation crucial. Large language models (LLMs) like ChatGPT and GPT-4offer a significant advance by generating literature recommendations without labeled datasets, but they face issues with generating unfaithful citations. Additionally, current tools lack the ability to provide precise, sentence- or paragraph-specific recommendations, highlighting the need for more granular solutions in biomedical research. To address the above limitations, we introduce the first sentence-level biomedical citation recommendation method BioCiteGPT based on retrieval-augmented LLMs, providing reliable citations for input sentences. To support this, we created the first open-source data suite with over 255K articles relevant to Alzheimer's disease and 528K sentences from these articles, each paired with positive citations and 15contrastive negatives, providing a comprehensive resource for model developing and testing. Our experiments show that this approach significantly improves the reliability and quality of LLM-generated citation recommendations.