Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Yan, Yukun"'
Autor:
Wang, Ruobing, Zha, Daren, Yu, Shi, Zhao, Qingfei, Chen, Yuxuan, Wang, Yixuan, Wang, Shuo, Yan, Yukun, Liu, Zhenghao, Han, Xu, Liu, Zhiyuan, Sun, Maosong
Retrieval-Augmented Generation (RAG) mitigates issues of the factual errors and hallucinated outputs generated by Large Language Models (LLMs) in open-domain question-answering tasks (OpenQA) via introducing external knowledge. For complex QA, howeve
Externí odkaz:
http://arxiv.org/abs/2410.08821
Autor:
Xu, Wang, Wang, Shuo, Zhao, Weilin, Han, Xu, Yan, Yukun, Zhang, Yudi, Tao, Zhe, Liu, Zhiyuan, Che, Wanxiang
Large language models (LLMs) have demonstrated the ability to improve human efficiency through conversational interactions. Conventional LLM-powered dialogue systems, operating on a turn-based paradigm, preclude real-time interaction during response
Externí odkaz:
http://arxiv.org/abs/2409.11727
Autor:
Yang, Weiqing, Wang, Hanbin, Liu, Zhenghao, Li, Xinze, Yan, Yukun, Wang, Shuo, Gu, Yu, Yu, Minghe, Liu, Zhiyuan, Yu, Ge
Debugging is a vital aspect of software development, yet the debugging capabilities of Large Language Models (LLMs) remain largely unexplored. This paper first introduces DEBUGEVAL, a comprehensive benchmark designed to evaluate the debugging capabil
Externí odkaz:
http://arxiv.org/abs/2408.05006
Autor:
Zhu, Kunlun, Luo, Yifan, Xu, Dingling, Wang, Ruobing, Yu, Shi, Wang, Shuo, Yan, Yukun, Liu, Zhenghao, Han, Xu, Liu, Zhiyuan, Sun, Maosong
Retrieval-Augmented Generation (RAG) systems have demonstrated their advantages in alleviating the hallucination of Large Language Models (LLMs). Existing RAG benchmarks mainly focus on evaluating whether LLMs can correctly answer the general knowled
Externí odkaz:
http://arxiv.org/abs/2408.01262
Autor:
Zeng, Zheni, Chen, Jiayi, Chen, Huimin, Yan, Yukun, Chen, Yuxuan, Liu, Zhenghao, Liu, Zhiyuan, Sun, Maosong
Large language models exhibit aspects of human-level intelligence that catalyze their application as human-like agents in domains such as social simulations, human-machine interactions, and collaborative multi-agent systems. However, the absence of d
Externí odkaz:
http://arxiv.org/abs/2407.12393
Autor:
Ping, Bowen, Wang, Shuo, Wang, Hanqing, Han, Xu, Xu, Yuzhuang, Yan, Yukun, Chen, Yun, Chang, Baobao, Liu, Zhiyuan, Sun, Maosong
Fine-tuning is a crucial process for adapting large language models (LLMs) to diverse applications. In certain scenarios, such as multi-tenant serving, deploying multiple LLMs becomes necessary to meet complex demands. Recent studies suggest decompos
Externí odkaz:
http://arxiv.org/abs/2406.08903
Large language models (LLMs) require lengthy prompts as the input context to produce output aligned with user intentions, a process that incurs extra costs during inference. In this paper, we propose the Gist COnditioned deCOding (Gist-COCO) model, i
Externí odkaz:
http://arxiv.org/abs/2402.16058
The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation, webpages can be used as a fundamental data resource for language mode
Externí odkaz:
http://arxiv.org/abs/2402.14652
Autor:
Xu, Zhipeng, Liu, Zhenghao, Liu, Yibin, Xiong, Chenyan, Yan, Yukun, Wang, Shuo, Yu, Shi, Liu, Zhiyuan, Yu, Ge
Retrieval Augmented Generation (RAG) has introduced a new paradigm for Large Language Models (LLMs), aiding in the resolution of knowledge-intensive tasks. However, current RAG models position LLMs as passive knowledge receptors, thereby restricting
Externí odkaz:
http://arxiv.org/abs/2402.13547
Autor:
Yang, Zhiyu, Zhou, Zihan, Wang, Shuo, Cong, Xin, Han, Xu, Yan, Yukun, Liu, Zhenghao, Tan, Zhixing, Liu, Pengyuan, Yu, Dong, Liu, Zhiyuan, Shi, Xiaodong, Sun, Maosong
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns. Despite its importance, the use of Large Language Models (LLMs) for scien
Externí odkaz:
http://arxiv.org/abs/2402.11453