Zobrazeno 1 - 10
of 10 161
pro vyhledávání: '"Li, JingJing"'
Augmenting Large Language Models (LLMs) with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses. Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the
Externí odkaz:
http://arxiv.org/abs/2406.17519
User studies are crucial for meeting user needs. In user studies, real experimental scenarios and participants are constructed and recruited. However, emerging and unfamiliar studies face limitations, including safety concerns and iterative efficienc
Externí odkaz:
http://arxiv.org/abs/2406.12296
Autor:
Hu, Minda, Zong, Licheng, Wang, Hongru, Zhou, Jingyan, Li, Jingjing, Gao, Yichen, Wong, Kam-Fai, Li, Yu, King, Irwin
Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented approaches face challenges in addressing diverse queries and document
Externí odkaz:
http://arxiv.org/abs/2406.11258
The role-play ability of Large Language Models (LLMs) has emerged as a popular research direction. However, existing studies focus on imitating well-known public figures or fictional characters, overlooking the potential for simulating ordinary indiv
Externí odkaz:
http://arxiv.org/abs/2404.13957
The next generation of very long baseline interferometry (VLBI) is stepping into the era of microarcsecond ($\mu$as) astronomy, and pushing astronomy, especially astrometry, to new heights. VLBI with the Square Kilometre Array (SKA), SKA-VLBI, will i
Externí odkaz:
http://arxiv.org/abs/2404.14663
Autor:
Sun, Hui, Pan, Bo, Yang, Zhening, Krajewski, Adam M., Bocklund, Brandon, Shang, Shun-Li, Li, Jingjing, Beese, Allison M., Liu, Zi-Kui
Publikováno v:
Materialia, Volume 36, August 2024, 102153
Assembly of dissimilar metals can be achieved by different methods, for example, casting, welding, and additive manufacturing (AM). However, undesired phases formed in liquid-phase assembling processes due to solute segregation during solidification
Externí odkaz:
http://arxiv.org/abs/2403.19084
Text watermarking algorithms for large language models (LLMs) can effectively identify machine-generated texts by embedding and detecting hidden features in the text. Although the current text watermarking algorithms perform well in most high-entropy
Externí odkaz:
http://arxiv.org/abs/2403.13485
Large vision-language models (VLMs) like CLIP have demonstrated good zero-shot learning performance in the unsupervised domain adaptation task. Yet, most transfer approaches for VLMs focus on either the language or visual branches, overlooking the nu
Externí odkaz:
http://arxiv.org/abs/2403.06946
Efficiently utilizing rich knowledge in pretrained models has become a critical topic in the era of large models. This work focuses on adaptively utilizing knowledge from multiple source-pretrained models to an unlabeled target domain without accessi
Externí odkaz:
http://arxiv.org/abs/2403.05062