Zobrazeno 1 - 10
of 23 557
pro vyhledávání: '"Biomedical knowledge"'
Autor:
Ma, Tengfei, Lin, Xuan, Li, Tianle, Li, Chaoyi, Chen, Long, Zhou, Peng, Cai, Xibao, Yang, Xinyu, Zeng, Daojian, Cao, Dongsheng, Zeng, Xiangxiang
Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we int
Externí odkaz:
http://arxiv.org/abs/2410.11550
Autor:
Cattaneo, Alberto, Bonner, Stephen, Martynec, Thomas, Luschi, Carlo, Barrett, Ian P, Justus, Daniel
Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models has bee
Externí odkaz:
http://arxiv.org/abs/2409.04103
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw
Externí odkaz:
http://arxiv.org/abs/2407.13492
The abundance of social media data allows researchers to construct large digital cohorts to study the interplay between human behavior and medical treatment. Identifying the users most relevant to a specific health problem is, however, a challenge in
Externí odkaz:
http://arxiv.org/abs/2405.07072
Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models. This paper presents a novel data augmentation technique to improve m
Externí odkaz:
http://arxiv.org/abs/2404.09206
Autor:
Xian, R. Patrick, Lee, Alex J., Lolla, Satvik, Wang, Vincent, Cui, Qiming, Ro, Russell, Abbasi-Asl, Reza
The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilities in high-stakes and knowledge-intensive tasks is essential for quanti
Externí odkaz:
http://arxiv.org/abs/2402.10527
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Allee, Nancy J., Perry, Gerald, Rios, Gabriel R., Rubin, Joshua C., Subbian, Vignesh, Swain, Deborah E., Wheeler, Terrie R.
Publikováno v:
Journal of the Medical Library Association. Apr2024, Vol. 112 Issue 2, p158-163. 6p.
Large language models (LLMs) are transforming the way information is retrieved with vast amounts of knowledge being summarized and presented via natural language conversations. Yet, LLMs are prone to highlight the most frequently seen pieces of infor
Externí odkaz:
http://arxiv.org/abs/2402.12352