Zobrazeno 1 - 10
of 488
pro vyhledávání: '"Zhou, Yujia"'
The learning and deployment of long-LLMs remains a challenging problem despite recent progresses. In this work, we argue that the long-LLMs are not a necessity to solve long-context tasks, as common long-context tasks are short-context solvable, i.e.
Externí odkaz:
http://arxiv.org/abs/2405.15318
Autor:
Li, Xiaoxi, Jin, Jiajie, Zhou, Yujia, Zhang, Yuyao, Zhang, Peitian, Zhu, Yutao, Dou, Zhicheng
Information Retrieval (IR) systems are crucial tools for users to access information, widely applied in scenarios like search engines, question answering, and recommendation systems. Traditional IR methods, based on similarity matching to return rank
Externí odkaz:
http://arxiv.org/abs/2404.14851
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate. This issue undermines the effectiveness of LLMs in practical applications, necessitating research into dete
Externí odkaz:
http://arxiv.org/abs/2403.06448
Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy. However, inconsistencies between retrieval kno
Externí odkaz:
http://arxiv.org/abs/2402.12174
Retrieval-augmented generation have become central in natural language processing due to their efficacy in generating factual content. While traditional methods employ single-time retrieval, more recent approaches have shifted towards multi-time retr
Externí odkaz:
http://arxiv.org/abs/2402.11626
Traditional search engines usually provide identical search results for all users, overlooking individual preferences. To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query
Externí odkaz:
http://arxiv.org/abs/2402.10548
This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems often struggle with grounding responses using precise evidence text due to
Externí odkaz:
http://arxiv.org/abs/2402.09760
Large language models (LLMs) have gained significant attention in various fields but prone to hallucination, especially in knowledge-intensive (KI) tasks. To address this, retrieval-augmented generation (RAG) has emerged as a popular solution to enha
Externí odkaz:
http://arxiv.org/abs/2402.01176
Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing. Existin
Externí odkaz:
http://arxiv.org/abs/2312.11036
Autor:
Xiang, Yang, Ji, Hangyu, Zhou, Yujia, Li, Fang, Du, Jingcheng, Rasmy, Laila, Wu, Stephen, Zheng, W Jim, Xu, Hua, Zhi, Degui, Zhang, Yaoyun, Tao, Cui
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 7, p e16981 (2020)
BackgroundAsthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients’ quality of life. However, efficient methods that can help identify personalized risk factors an
Externí odkaz:
https://doaj.org/article/01ac11bbb3604ee3bbfa8dd4555a7f8f