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pro vyhledávání: '"Dou, Zhicheng"'
AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant
The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hallucination". Initial retrieval-augmented generation (RAG) methods like
Externí odkaz:
http://arxiv.org/abs/2411.06805
Autor:
Deng, Zhirui, Dou, Zhicheng, Zhu, Yutao, Wen, Ji-Rong, Xiong, Ruibin, Wang, Mang, Chen, Weipeng
The outstanding capabilities of large language models (LLMs) render them a crucial component in various autonomous agent systems. While traditional methods depend on the inherent knowledge of LLMs without fine-tuning, more recent approaches have shif
Externí odkaz:
http://arxiv.org/abs/2411.03817
Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG systems, and many commercial systems such as ChatGPT an
Externí odkaz:
http://arxiv.org/abs/2411.02959
Autor:
Cheng, Yiruo, Mao, Kelong, Zhao, Ziliang, Dong, Guanting, Qian, Hongjin, Wu, Yongkang, Sakai, Tetsuya, Wen, Ji-Rong, Dou, Zhicheng
Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG
Externí odkaz:
http://arxiv.org/abs/2410.23090
Synthetic data generation has become an increasingly popular way of training models without the need for large, manually labeled datasets. For tasks like text embedding, synthetic data offers diverse and scalable training examples, significantly redu
Externí odkaz:
http://arxiv.org/abs/2410.18634
Autor:
Mo, Fengran, Mao, Kelong, Zhao, Ziliang, Qian, Hongjin, Chen, Haonan, Cheng, Yiruo, Li, Xiaoxi, Zhu, Yutao, Dou, Zhicheng, Nie, Jian-Yun
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) technologies, particularly large language models (LLMs), search engines
Externí odkaz:
http://arxiv.org/abs/2410.15576
Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. Despite recent advancements in Large Language Models (LLMs), research on assessing and improving instruction-following (IF) align
Externí odkaz:
http://arxiv.org/abs/2410.09584
Autor:
Liu, Jiongnan, Zhu, Yutao, Wang, Shuting, Wei, Xiaochi, Min, Erxue, Lu, Yu, Wang, Shuaiqiang, Yin, Dawei, Dou, Zhicheng
Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests. This has led to the development of various personalized approach
Externí odkaz:
http://arxiv.org/abs/2409.11901
Autor:
Zhou, Yujia, Liu, Yan, Li, Xiaoxi, Jin, Jiajie, Qian, Hongjin, Liu, Zheng, Li, Chaozhuo, Dou, Zhicheng, Ho, Tsung-Yi, Yu, Philip S.
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). While much of the current research in this field focuses on performance optimization, particularly in terms of accuracy
Externí odkaz:
http://arxiv.org/abs/2409.10102
Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases, thereby enhancing the generation quality of large language models (LLMs) through optimized context. However, the existing retrieval methods are constrained i
Externí odkaz:
http://arxiv.org/abs/2409.05591