Zobrazeno 1 - 10
of 717
pro vyhledávání: '"Zheng Tianyu"'
We introduce SimulBench, a benchmark designed to evaluate large language models (LLMs) across a diverse collection of creative simulation scenarios, such as acting as a Linux terminal or playing text games with users. While these simulation tasks ser
Externí odkaz:
http://arxiv.org/abs/2409.07641
Autor:
Yue, Xiang, Zheng, Tianyu, Ni, Yuansheng, Wang, Yubo, Zhang, Kai, Tong, Shengbang, Sun, Yuxuan, Yu, Botao, Zhang, Ge, Sun, Huan, Su, Yu, Chen, Wenhu, Neubig, Graham
This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models' true understanding and reasoning capabilities through a three-st
Externí odkaz:
http://arxiv.org/abs/2409.02813
Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency. This paper explores the historical development of SNN and co
Externí odkaz:
http://arxiv.org/abs/2408.13996
Autor:
Liang, Yiming, Zhang, Ge, Qu, Xingwei, Zheng, Tianyu, Guo, Jiawei, Du, Xinrun, Yang, Zhenzhu, Liu, Jiaheng, Lin, Chenghua, Ma, Lei, Huang, Wenhao, Zhang, Jiajun
Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potential for active learning and alignment. Some approaches train LLMs using
Externí odkaz:
http://arxiv.org/abs/2408.08072
Autor:
Wang, Leyan, Jin, Yonggang, Shen, Tianhao, Zheng, Tianyu, Du, Xinrun, Zhang, Chenchen, Huang, Wenhao, Liu, Jiaheng, Wang, Shi, Zhang, Ge, Xiang, Liuyu, He, Zhaofeng
As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their perspectives is increasingly recognized as critical. Most existing bench
Externí odkaz:
http://arxiv.org/abs/2406.14903
In the realm of mimicking human deliberation, large language models (LLMs) show promising performance, thereby amplifying the importance of this research area. Deliberation is influenced by both logic and personality. However, previous studies predom
Externí odkaz:
http://arxiv.org/abs/2404.07084
Autor:
Qu, Xingwei, Bai, Yuelin, Ma, Yinghao, Zhou, Ziya, Lo, Ka Man, Liu, Jiaheng, Yuan, Ruibin, Min, Lejun, Liu, Xueling, Zhang, Tianyu, Du, Xinrun, Guo, Shuyue, Liang, Yiming, Li, Yizhi, Wu, Shangda, Zhou, Junting, Zheng, Tianyu, Ma, Ziyang, Han, Fengze, Xue, Wei, Xia, Gus, Benetos, Emmanouil, Yue, Xiang, Lin, Chenghua, Tan, Xu, Huang, Stephen W., Fu, Jie, Zhang, Ge
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our findings suggest that LLMs are inherently more compatible with ABC Nota
Externí odkaz:
http://arxiv.org/abs/2404.06393
Autor:
Du, Xinrun, Yu, Zhouliang, Gao, Songyang, Pan, Ding, Cheng, Yuyang, Ma, Ziyang, Yuan, Ruibin, Qu, Xingwei, Liu, Jiaheng, Zheng, Tianyu, Luo, Xinchen, Zhou, Guorui, Chen, Wenhu, Zhang, Ge
In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs. Uniquely initiated from scratch, CT-LLM diverges from the conventional methodology by p
Externí odkaz:
http://arxiv.org/abs/2404.04167
Autor:
Guo, Jiawei, Li, Ziming, Liu, Xueling, Ma, Kaijing, Zheng, Tianyu, Yu, Zhouliang, Pan, Ding, LI, Yizhi, Liu, Ruibo, Wang, Yue, Guo, Shuyue, Qu, Xingwei, Yue, Xiang, Zhang, Ge, Chen, Wenhu, Fu, Jie
Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to rigorously assess the performance of LLMs in code editing tasks, includ
Externí odkaz:
http://arxiv.org/abs/2404.03543
Autor:
Bai, Yuelin, Du, Xinrun, Liang, Yiming, Jin, Yonggang, Zhou, Junting, Liu, Ziqiang, Fang, Feiteng, Chang, Mingshan, Zheng, Tianyu, Zhang, Xincheng, Ma, Nuo, Wang, Zekun, Yuan, Ruibin, Wu, Haihong, Lin, Hongquan, Huang, Wenhao, Zhang, Jiajun, Lin, Chenghua, Fu, Jie, Yang, Min, Ni, Shiwen, Zhang, Ge
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instruction tuning for Chinese, where the complex linguistic features pose sign
Externí odkaz:
http://arxiv.org/abs/2403.18058