Retrieval-augmented generation (RAG) has attracted significant attention for enhancing large language models (LLMs) in domain-specific and knowledge-intensive tasks by utilizing external documents retrieved by retrievers. However, LLMs often struggle to determine which retrieved documents are relevant and how they rela...
Fu-Da Ye, Shuang-Yin Li, Yong-Qi Zhang et al.· ACM Transactions on Knowledg...· 0 citations
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Hao-Yang Li et al.· Proceedings of the 32nd ACM...· 0 citations
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
MolGlass is proposed, a data-centric paradigm for visual molecular understanding in vision-language models (VLMs) that injects chemical priors directly into the visual input through chemical-aware visual augmentations, without modifying model architectures or training molecule-specific encoders.
Runqing Xu, Xiaotang Wang, Chunfeng Gao et al.· Proceedings of the 32nd ACM...· 0 citations
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