Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Ji, Jianchao"'
In the rapidly evolving field of artificial intelligence, large language models (LLMs) have emerged as powerful tools for a myriad of applications, from natural language processing to decision-making support systems. However, as these models become i
Externí odkaz:
http://arxiv.org/abs/2406.04428
Autor:
Lin, Shuhang, Hua, Wenyue, Li, Lingyao, Chang, Che-Jui, Fan, Lizhou, Ji, Jianchao, Hua, Hang, Jin, Mingyu, Luo, Jiebo, Zhang, Yongfeng
This paper presents BattleAgent, an emulation system that combines the Large Vision-Language Model and Multi-agent System. This novel system aims to simulate complex dynamic interactions among multiple agents, as well as between agents and their envi
Externí odkaz:
http://arxiv.org/abs/2404.15532
Recently emerged prompt-based Recommendation Language Models (RLM) can solve multiple recommendation tasks uniformly. The RLMs make full use of the inherited knowledge learned from the abundant pre-training data to solve the downstream recommendation
Externí odkaz:
http://arxiv.org/abs/2402.00284
Autor:
Hua, Wenyue, Fan, Lizhou, Li, Lingyao, Mei, Kai, Ji, Jianchao, Ge, Yingqiang, Hemphill, Libby, Zhang, Yongfeng
Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research, we attempt to answer the question based on the recent advances of Art
Externí odkaz:
http://arxiv.org/abs/2311.17227
Autor:
Tan, Juntao, Ge, Yingqiang, Zhu, Yan, Xia, Yinglong, Luo, Jiebo, Ji, Jianchao, Zhang, Yongfeng
Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recen
Externí odkaz:
http://arxiv.org/abs/2308.00894
Autor:
Ji, Jianchao, Li, Zelong, Xu, Shuyuan, Hua, Wenyue, Ge, Yingqiang, Tan, Juntao, Zhang, Yongfeng
In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. Th
Externí odkaz:
http://arxiv.org/abs/2307.00457
Autor:
Ji, Jianchao, Li, Zelong, Xu, Shuyuan, Xiong, Max, Tan, Juntao, Ge, Yingqiang, Wang, Hao, Zhang, Yongfeng
Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reas
Externí odkaz:
http://arxiv.org/abs/2307.00165
Recent advances in Foundation Models such as Large Language Models (LLMs) have propelled them to the forefront of Recommender Systems (RS). Despite their utility, there is a growing concern that LLMs might inadvertently perpetuate societal stereotype
Externí odkaz:
http://arxiv.org/abs/2305.12090
Autor:
Ge, Yingqiang, Hua, Wenyue, Mei, Kai, Ji, Jianchao, Tan, Juntao, Xu, Shuyuan, Li, Zelong, Zhang, Yongfeng
Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solv
Externí odkaz:
http://arxiv.org/abs/2304.04370
Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, relying sol
Externí odkaz:
http://arxiv.org/abs/2301.04016