Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Jiang, Haoming"'
Autor:
Cheng, Kewei, Yang, Jingfeng, Jiang, Haoming, Wang, Zhengyang, Huang, Binxuan, Li, Ruirui, Li, Shiyang, Li, Zheng, Gao, Yifan, Li, Xian, Yin, Bing, Sun, Yizhou
Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), most studies have failed to rigorously differentiate between inductive a
Externí odkaz:
http://arxiv.org/abs/2408.00114
Large language models (LLMs) excel in many natural language processing (NLP) tasks. However, since LLMs can only incorporate new knowledge through training or supervised fine-tuning processes, they are unsuitable for applications that demand precise,
Externí odkaz:
http://arxiv.org/abs/2407.15071
Autor:
Bukharin, Alexander, Hong, Ilgee, Jiang, Haoming, Li, Zichong, Zhang, Qingru, Zhang, Zixuan, Zhao, Tuo
Reinforcement learning from human feedback (RLHF) provides a principled framework for aligning AI systems with human preference data. For various reasons, e.g., personal bias, context ambiguity, lack of training, etc, human annotators may give incorr
Externí odkaz:
http://arxiv.org/abs/2406.15568
Autor:
Hong, Ilgee, Li, Zichong, Bukharin, Alexander, Li, Yixiao, Jiang, Haoming, Yang, Tianbao, Zhao, Tuo
Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values by learning rewards from human preference data. Due to various reasons, however, such data typically takes the form of rankings over pairs
Externí odkaz:
http://arxiv.org/abs/2406.02764
Autor:
Wang, Haoyu, Li, Ruirui, Jiang, Haoming, Tian, Jinjin, Wang, Zhengyang, Luo, Chen, Tang, Xianfeng, Cheng, Monica, Zhao, Tuo, Gao, Jing
Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios. However, these methods often face challenges with complex inputs and encounter difficulties due to noisy knowle
Externí odkaz:
http://arxiv.org/abs/2402.11129
Autor:
Wang, Yu, Gao, Yifan, Chen, Xiusi, Jiang, Haoming, Li, Shiyang, Yang, Jingfeng, Yin, Qingyu, Li, Zheng, Li, Xian, Yin, Bing, Shang, Jingbo, McAuley, Julian
Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a considerable portion of self-updatable parameters, enabling the model t
Externí odkaz:
http://arxiv.org/abs/2402.04624
Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of event sequences, which involves calculating some intractable integral.
Externí odkaz:
http://arxiv.org/abs/2310.16336
Autor:
Zhu, Zining, Jiang, Haoming, Yang, Jingfeng, Nag, Sreyashi, Zhang, Chao, Huang, Jie, Gao, Yifan, Rudzicz, Frank, Yin, Bing
Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to generate coherent natural language explanations (NLE). The existing NLE rese
Externí odkaz:
http://arxiv.org/abs/2308.14115
Autor:
Jin, Wei, Mao, Haitao, Li, Zheng, Jiang, Haoming, Luo, Chen, Wen, Hongzhi, Han, Haoyu, Lu, Hanqing, Wang, Zhengyang, Li, Ruirui, Li, Zhen, Cheng, Monica Xiao, Goutam, Rahul, Zhang, Haiyang, Subbian, Karthik, Wang, Suhang, Sun, Yizhou, Tang, Jiliang, Yin, Bing, Tang, Xianfeng
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based
Externí odkaz:
http://arxiv.org/abs/2307.09688
Autor:
Li, Shiyang, Gao, Yifan, Jiang, Haoming, Yin, Qingyu, Li, Zheng, Yan, Xifeng, Zhang, Chao, Yin, Bing
Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which are then fed into KG encoder, e.g. graph neural networks (GNNs), to mode
Externí odkaz:
http://arxiv.org/abs/2305.18742