Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Zeng, Xingshan"'
Autor:
Liu, Weiwen, Huang, Xu, Zeng, Xingshan, Hao, Xinlong, Yu, Shuai, Li, Dexun, Wang, Shuai, Gan, Weinan, Liu, Zhengying, Yu, Yuanqing, Wang, Zezhong, Wang, Yuxian, Ning, Wu, Hou, Yutai, Wang, Bin, Wu, Chuhan, Wang, Xinzhi, Liu, Yong, Wang, Yasheng, Tang, Duyu, Tu, Dandan, Shang, Lifeng, Jiang, Xin, Tang, Ruiming, Lian, Defu, Liu, Qun, Chen, Enhong
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. However, real function-calling data is quite challenging to collect and
Externí odkaz:
http://arxiv.org/abs/2409.00920
Autor:
Jiang, Yuxin, Huang, Bo, Wang, Yufei, Zeng, Xingshan, Li, Liangyou, Wang, Yasheng, Jiang, Xin, Shang, Lifeng, Tang, Ruiming, Wang, Wei
Direct preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pairwise preference data. However, the winning response and the losing res
Externí odkaz:
http://arxiv.org/abs/2408.07471
Autor:
Wang, Zezhong, Zeng, Xingshan, Liu, Weiwen, Wang, Yufei, Li, Liangyou, Wang, Yasheng, Shang, Lifeng, Jiang, Xin, Liu, Qun, Wong, Kam-Fai
Current research found the issue of Early Answering in large language models (LLMs), where the models already have an answer before generating the Chain-of-Thought (CoT). This phenomenon suggests a potential lack of necessary dependency between the p
Externí odkaz:
http://arxiv.org/abs/2406.16144
Autor:
Jiang, Yuxin, Wang, Yufei, Wu, Chuhan, Zhong, Wanjun, Zeng, Xingshan, Gao, Jiahui, Li, Liangyou, Jiang, Xin, Shang, Lifeng, Tang, Ruiming, Liu, Qun, Wang, Wei
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods pre
Externí odkaz:
http://arxiv.org/abs/2402.11905
The aim of this study is to investigate Machine Unlearning (MU), a burgeoning field focused on addressing concerns related to neural models inadvertently retaining personal or sensitive data. Here, a novel approach is introduced to achieve precise an
Externí odkaz:
http://arxiv.org/abs/2402.05813
Autor:
Huang, Shijue, Zhong, Wanjun, Lu, Jianqiao, Zhu, Qi, Gao, Jiahui, Liu, Weiwen, Hou, Yutai, Zeng, Xingshan, Wang, Yasheng, Shang, Lifeng, Jiang, Xin, Xu, Ruifeng, Liu, Qun
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using
Externí odkaz:
http://arxiv.org/abs/2401.17167
Autor:
Kwan, Wai-Chung, Zeng, Xingshan, Jiang, Yuxin, Wang, Yufei, Li, Liangyou, Shang, Lifeng, Jiang, Xin, Liu, Qun, Wong, Kam-Fai
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications. However, existing benchmarks predominantly focus on single-turn evaluations, overlooking the models' capabilities i
Externí odkaz:
http://arxiv.org/abs/2401.16745
Autor:
Pankov, Vikentii, Pronina, Valeria, Kuzmin, Alexander, Borisov, Maksim, Usoltsev, Nikita, Zeng, Xingshan, Golubkov, Alexander, Ermolenko, Nikolai, Shirshova, Aleksandra, Matveeva, Yulia
We address zero-shot TTS systems' noise-robustness problem by proposing a dual-objective training for the speaker encoder using self-supervised DINO loss. This approach enhances the speaker encoder with the speech synthesis objective, capturing a wid
Externí odkaz:
http://arxiv.org/abs/2311.09770
Autor:
Jiang, Yuxin, Wang, Yufei, Zeng, Xingshan, Zhong, Wanjun, Li, Liangyou, Mi, Fei, Shang, Lifeng, Jiang, Xin, Liu, Qun, Wang, Wei
The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure response quality, rather than assessing whether the response follows cons
Externí odkaz:
http://arxiv.org/abs/2310.20410
Autor:
Kwan, Wai-Chung, Zeng, Xingshan, Wang, Yufei, Sun, Yusen, Li, Liangyou, Shang, Lifeng, Liu, Qun, Wong, Kam-Fai
Managing long sequences has become an important and necessary feature for large language models (LLMs). However, it is still an open question of how to comprehensively and systematically evaluate the long-sequence capability of LLMs. One of the reaso
Externí odkaz:
http://arxiv.org/abs/2310.19240