Zobrazeno 1 - 10
of 11 364
pro vyhledávání: '"Yan Dong An"'
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
We perform a probabilistic investigation on the effect of systematically removing imperfections on the buckling behavior of pressurized thin, elastic, hemispherical shells containing a distribution of defects. We employ finite element simulations, wh
Externí odkaz:
http://arxiv.org/abs/2410.08973
Autor:
Ye, Ziyi, Li, Xiangsheng, Li, Qiuchi, Ai, Qingyao, Zhou, Yujia, Shen, Wei, Yan, Dong, Liu, Yiqun
Learning from preference feedback is a common practice for aligning large language models~(LLMs) with human value. Conventionally, preference data is learned and encoded into a scalar reward model that connects a value head with an LLM to produce a s
Externí odkaz:
http://arxiv.org/abs/2410.03742
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
We present a robust refinement method for estimating oriented normals from unstructured point clouds. In contrast to previous approaches that either suffer from high computational complexity or fail to achieve desirable accuracy, our novel framework
Externí odkaz:
http://arxiv.org/abs/2409.01100
This paper presents a novel non-rigid point set registration method that is inspired by unsupervised clustering analysis. Unlike previous approaches that treat the source and target point sets as separate entities, we develop a holistic framework whe
Externí odkaz:
http://arxiv.org/abs/2406.18817
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
Point cloud normal estimation is a fundamental task in 3D geometry processing. While recent learning-based methods achieve notable advancements in normal prediction, they often overlook the critical aspect of equivariance. This results in inefficient
Externí odkaz:
http://arxiv.org/abs/2406.00347
This paper presents an in-depth examination of the evolution and interplay of cognitive and expressive capabilities in large language models (LLMs), with a specific focus on Baichuan-7B and Baichuan-33B, an advanced bilingual (Chinese and English) LL
Externí odkaz:
http://arxiv.org/abs/2405.16964