Zobrazeno 1 - 10
of 7 247
pro vyhledávání: '"Xie,Jian"'
Autor:
Zhang, Hanyu, Qiu, Boyu, Feng, Yuhao, Li, Shuqi, Ma, Qian, Zhang, Xiyuan, Ju, Qiang, Yan, Dong, Xie, Jian
Large language models (LLMs) have demonstrated strong capabilities in language understanding, generation, and reasoning, yet their potential in finance remains underexplored due to the complexity and specialization of financial knowledge. In this wor
Externí odkaz:
http://arxiv.org/abs/2412.15270
Autor:
Jiang, Jinhao, Chen, Zhipeng, Min, Yingqian, Chen, Jie, Cheng, Xiaoxue, Wang, Jiapeng, Tang, Yiru, Sun, Haoxiang, Deng, Jia, Zhao, Wayne Xin, Liu, Zheng, Yan, Dong, Xie, Jian, Wang, Zhongyuan, Wen, Ji-Rong
Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By allocating more computational resources during the inference phase, large l
Externí odkaz:
http://arxiv.org/abs/2411.11694
Autor:
Lou, Renze, Xu, Hanzi, Wang, Sijia, Du, Jiangshu, Kamoi, Ryo, Lu, Xiaoxin, Xie, Jian, Sun, Yuxuan, Zhang, Yusen, Ahn, Jihyun Janice, Fang, Hongchao, Zou, Zhuoyang, Ma, Wenchao, Li, Xi, Zhang, Kai, Xia, Congying, Huang, Lifu, Yin, Wenpeng
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation. However, researchers face unique chall
Externí odkaz:
http://arxiv.org/abs/2410.22394
Autor:
Xie, Jian, Zhang, Kexun, Chen, Jiangjie, Yuan, Siyu, Zhang, Kai, Zhang, Yikai, Li, Lei, Xiao, Yanghua
Autonomous planning has been an ongoing pursuit since the inception of artificial intelligence. Based on curated problem solvers, early planning agents could deliver precise solutions for specific tasks but lacked generalization. The emergence of lar
Externí odkaz:
http://arxiv.org/abs/2410.12409
Logical reasoning is a crucial task for Large Language Models (LLMs), enabling them to tackle complex problems. Among reasoning tasks, multi-step reasoning poses a particular challenge. Grounded in the theory of formal logic, we have developed an aut
Externí odkaz:
http://arxiv.org/abs/2410.09528
Reward models (RM) play a critical role in aligning generations of large language models (LLM) to human expectations. However, prevailing RMs fail to capture the stochasticity within human preferences and cannot effectively evaluate the reliability o
Externí odkaz:
http://arxiv.org/abs/2410.00847
Autor:
Yan, Yuzi, Lou, Xingzhou, Li, Jialian, Zhang, Yiping, Xie, Jian, Yu, Chao, Wang, Yu, Yan, Dong, Shen, Yuan
As Large Language Models (LLMs) continue to progress toward more advanced forms of intelligence, Reinforcement Learning from Human Feedback (RLHF) is increasingly seen as a key pathway toward achieving Artificial General Intelligence (AGI). However,
Externí odkaz:
http://arxiv.org/abs/2409.15360
Existing agents based on large language models (LLMs) demonstrate robust problem-solving capabilities by integrating LLMs' inherent knowledge, strong in-context learning and zero-shot capabilities, and the use of tools combined with intricately desig
Externí odkaz:
http://arxiv.org/abs/2407.10718
Autor:
Ju, Tianjie, Wang, Yiting, Ma, Xinbei, Cheng, Pengzhou, Zhao, Haodong, Wang, Yulong, Liu, Lifeng, Xie, Jian, Zhang, Zhuosheng, Liu, Gongshen
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solving and autonomous negotiation. However, the security implications of t
Externí odkaz:
http://arxiv.org/abs/2407.07791
Aligning large language models (LLMs) with human preference has recently gained tremendous attention, with the canonical yet costly RLHF-PPO and the simple and straightforward Direct Preference Optimization (DPO) as two examples. Despite the efficien
Externí odkaz:
http://arxiv.org/abs/2406.07327