Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Wang, Haochun"'
Artificial Intelligence predicts drug properties by encoding drug molecules, aiding in the rapid screening of candidates. Different molecular representations, such as SMILES and molecule graphs, contain complementary information for molecular encodin
Externí odkaz:
http://arxiv.org/abs/2406.18020
Chain-of-Thought (CoT) serves as a critical emerging ability in LLMs, especially when it comes to logical reasoning. Attempts have been made to induce such ability in small models as well by distilling from the data with CoT generated by Large Langua
Externí odkaz:
http://arxiv.org/abs/2403.01969
In the field of natural language processing (NLP), Large Language Models (LLMs) have precipitated a paradigm shift, markedly enhancing performance in natural language generation tasks. Despite these advancements, the comprehensive evaluation of LLMs
Externí odkaz:
http://arxiv.org/abs/2402.01349
Automatic diagnosis is a significant application of AI in healthcare, where diagnoses are generated based on the symptom description of patients. Previous works have approached this task directly by modeling the relationship between the normalized sy
Externí odkaz:
http://arxiv.org/abs/2401.16107
Deep learning is now widely used in drug discovery, providing significant acceleration and cost reduction. As the most fundamental building block, molecular representation is essential for predicting molecular properties to enable various downstream
Externí odkaz:
http://arxiv.org/abs/2401.11403
Autor:
Du, Yanrui, Zhao, Sendong, Wang, Haochun, Chen, Yuhan, Bai, Rui, Qiang, Zewen, Cai, Muzhen, Qin, Bing
Explaining black-box model behavior with natural language has achieved impressive results in various NLP tasks. Recent research has explored the utilization of subsequences from the input text as a rationale, providing users with evidence to support
Externí odkaz:
http://arxiv.org/abs/2310.13610
Molecule discovery serves as a cornerstone in numerous scientific domains, fueling the development of new materials and innovative drug designs. Recent developments of in-silico molecule discovery have highlighted the promising results of cross-modal
Externí odkaz:
http://arxiv.org/abs/2309.05203
Prompt-based classification adapts tasks to a cloze question format utilizing the [MASK] token and the filled tokens are then mapped to labels through pre-defined verbalizers. Recent studies have explored the use of verbalizer embeddings to reduce la
Externí odkaz:
http://arxiv.org/abs/2309.04174
Autor:
Wang, Haochun, Zhao, Sendong, Qiang, Zewen, Li, Zijian, Xi, Nuwa, Du, Yanrui, Cai, MuZhen, Guo, Haoqiang, Chen, Yuhan, Xu, Haoming, Qin, Bing, Liu, Ting
Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the hallucination about medical facts due to limited domain knowl
Externí odkaz:
http://arxiv.org/abs/2309.04175
Decoding text stimuli from cognitive signals (e.g. fMRI) enhances our understanding of the human language system, paving the way for building versatile Brain-Computer Interface. However, existing studies largely focus on decoding individual word-leve
Externí odkaz:
http://arxiv.org/abs/2307.05355