Zobrazeno 1 - 10
of 367
pro vyhledávání: '"Li, Zelong"'
Autor:
Hua, Wenyue, Zhu, Kaijie, Li, Lingyao, Fan, Lizhou, Lin, Shuhang, Jin, Mingyu, Xue, Haochen, Li, Zelong, Wang, JinDong, Zhang, Yongfeng
This study intends to systematically disentangle pure logic reasoning and text understanding by investigating the contrast across abstract and contextualized logical problems from a comprehensive set of domains. We explore whether LLMs demonstrate ge
Externí odkaz:
http://arxiv.org/abs/2406.02787
AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents
Since their inception, programming languages have trended towards greater readability and lower barriers for programmers. Following this trend, natural language can be a promising type of programming language that provides great flexibility and usabi
Externí odkaz:
http://arxiv.org/abs/2405.06907
Generative recommendation based on Large Language Models (LLMs) have transformed the traditional ranking-based recommendation style into a text-to-text generation paradigm. However, in contrast to standard NLP tasks that inherently operate on human v
Externí odkaz:
http://arxiv.org/abs/2403.19021
The integration and deployment of large language model (LLM)-based intelligent agents have been fraught with challenges that compromise their efficiency and efficacy. Among these issues are sub-optimal scheduling and resource allocation of agent requ
Externí odkaz:
http://arxiv.org/abs/2403.16971
The emergence of LLM-based agents has garnered considerable attention, yet their trustworthiness remains an under-explored area. As agents can directly interact with the physical environment, their reliability and safety is critical. This paper prese
Externí odkaz:
http://arxiv.org/abs/2402.01586
Recent advancements on Large Language Models (LLMs) enable AI Agents to automatically generate and execute multi-step plans to solve complex tasks. However, since LLM's content generation process is hardly controllable, current LLM-based agents frequ
Externí odkaz:
http://arxiv.org/abs/2402.00798
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
In this paper we verify that with the exception of the $(2, 2n+1)$ torus knots, positive 2-bridge knots up to 31 crossings do not admit chirally cosmetic surgeries. A knot $K$ admits chirally cosmetic surgeries if there exist surgeries $S^3_r$ and $S
Externí odkaz:
http://arxiv.org/abs/2308.10126
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