Zobrazeno 1 - 10
of 429
pro vyhledávání: '"D'Ermo A"'
Autor:
Ding, Ning, Qu, Shang, Xie, Linhai, Li, Yifei, Liu, Zaoqu, Zhang, Kaiyan, Xiong, Yibai, Zuo, Yuxin, Chen, Zhangren, Hua, Ermo, Lv, Xingtai, Sun, Youbang, Li, Yang, Li, Dong, He, Fuchu, Zhou, Bowen
With the development of artificial intelligence, its contribution to science is evolving from simulating a complex problem to automating entire research processes and producing novel discoveries. Achieving this advancement requires both specialized g
Externí odkaz:
http://arxiv.org/abs/2411.03743
Improving the effectiveness and efficiency of large language models (LLMs) simultaneously is a critical yet challenging research goal. In this paper, we find that low-rank pre-training, normally considered as efficient methods that will compromise pe
Externí odkaz:
http://arxiv.org/abs/2411.02063
Autor:
Fan, Yuchen, Zhong, Xin, Zhou, Heng, Zhang, Yuchen, Liang, Mingyu, Xie, Chengxing, Hua, Ermo, Ding, Ning, Zhou, Bowen
Long-Form Question Answering (LFQA) refers to generating in-depth, paragraph-level responses to open-ended questions. Although lots of LFQA methods are developed, evaluating LFQA effectively and efficiently remains challenging due to its high complex
Externí odkaz:
http://arxiv.org/abs/2410.01945
Autor:
Qi, Biqing, Zhang, Kaiyan, Tian, Kai, Li, Haoxiang, Chen, Zhang-Ren, Zeng, Sihang, Hua, Ermo, Jinfang, Hu, Zhou, Bowen
The rapid growth of biomedical knowledge has outpaced our ability to efficiently extract insights and generate novel hypotheses. Large language models (LLMs) have emerged as a promising tool to revolutionize knowledge interaction and potentially acce
Externí odkaz:
http://arxiv.org/abs/2407.08940
Large Language Models (LLMs) exhibit impressive capabilities across various applications but encounter substantial challenges such as high inference latency, considerable training costs, and the generation of hallucinations. Collaborative decoding be
Externí odkaz:
http://arxiv.org/abs/2406.12295
Autor:
Zhang, Kaiyan, Zeng, Sihang, Hua, Ermo, Ding, Ning, Chen, Zhang-Ren, Ma, Zhiyuan, Li, Haoxin, Cui, Ganqu, Qi, Biqing, Zhu, Xuekai, Lv, Xingtai, Jinfang, Hu, Liu, Zhiyuan, Zhou, Bowen
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such as GPT-4 and Gemini have achieved significant advancements in biomedi
Externí odkaz:
http://arxiv.org/abs/2406.03949
Autor:
Hua, Ermo, Qi, Biqing, Zhang, Kaiyan, Yu, Yue, Ding, Ning, Lv, Xingtai, Tian, Kai, Zhou, Bowen
Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are two fundamental processes for enhancing the capabilities of Language Models (LMs) post pre-training, aligning them better with human preferences. Although SFT advances in training effi
Externí odkaz:
http://arxiv.org/abs/2405.11870
With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their deployment (especially for smaller ones) on personal devices, such as PCs and smartphones, has become a prevailing trend. In contexts
Externí odkaz:
http://arxiv.org/abs/2403.03129
Autor:
Tommaso Di Libero, Lavinia Falese, Annalisa D’Ermo, Beatrice Tosti, Stefano Corrado, Alice Iannaccone, Pierluigi Diotaiuti, Angelo Rodio
Publikováno v:
Journal of Functional Morphology and Kinesiology, Vol 9, Iss 3, p 170 (2024)
Background: The COVID-19 pandemic has led to reduced physical activity and increased sedentary behaviors, negatively impacting mental and physical health. Engaging in physical activity at home during quarantine became essential to counteracting these
Externí odkaz:
https://doaj.org/article/93b4f84966bf48448b058a974e45e7c5
Autor:
Ermo Chen
Publikováno v:
Cost Effectiveness and Resource Allocation, Vol 21, Iss 1, Pp 1-14 (2023)
Abstract Dealing with randomness is a crucial aspect that cost-effectiveness analysis (CEA) tools need to address, but existing stochastic CEA tools have rarely examined risk and return from the perspective of population benefits, concerning the bene
Externí odkaz:
https://doaj.org/article/047ea5a395fb48499906169a6378790c