Zobrazeno 1 - 10
of 418
pro vyhledávání: '"Liu, ZhengHao"'
Autor:
Zeng, Yang, Lei, Yihan, Wang, Yanghe, Cheng, Mingqiang, Liao, Luocheng, Wang, Xuyang, Ge, Jinxin, Liu, Zhenghao, Ming, Wenjie, Li, Chao, Xie, Shuhong, Li, Jiangyu, Li, Changjian
Piezoelectric and ferroelectric wurtzite are promising to reshape modern microelectronics because they can be easily integrated with mainstream semiconductor technology. Sc doped AlN (Al$_{1-x}$Sc$_x$N) has attracted much attention for its enhanced p
Externí odkaz:
http://arxiv.org/abs/2408.11379
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
Dynamic graphs are pervasive in the real world, modeling dynamic relations between objects across various fields. For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique, which are generally pre-traine
Externí odkaz:
http://arxiv.org/abs/2405.13937
Fact verification tasks aim to identify the integrity of textual contents according to the truthful corpus. Existing fact verification models usually build a fully connected reasoning graph, which regards claim-evidence pairs as nodes and connects th
Externí odkaz:
http://arxiv.org/abs/2405.10481
Autor:
Yuan, Lifan, Cui, Ganqu, Wang, Hanbin, Ding, Ning, Wang, Xingyao, Deng, Jia, Shan, Boji, Chen, Huimin, Xie, Ruobing, Lin, Yankai, Liu, Zhenghao, Zhou, Bowen, Peng, Hao, Liu, Zhiyuan, Sun, Maosong
We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve state-of-the-art results among open-source models on a diverse set of benchmarks covering mathemati
Externí odkaz:
http://arxiv.org/abs/2404.02078
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:
Lu, Luming, An, Jiyuan, Wang, Yujie, yang, Liner, Kong, Cunliang, Liu, Zhenghao, Wang, Shuo, Lin, Haozhe, Fang, Mingwei, Huang, Yaping, Yang, Erhong
Natural Language Processing (NLP) technologies have revolutionized the way we interact with information systems, with a significant focus on converting natural language queries into formal query languages such as SQL. However, less emphasis has been
Externí odkaz:
http://arxiv.org/abs/2402.13740