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cs.CL2025

PowerAttention: Exponentially Scaling of Receptive Fields for Effective Sparse Attention

Lida Chen, Dong Xu, Chenxin An +8

Large Language Models (LLMs) face efficiency bottlenecks due to the quadratic complexity of the attention mechanism when processing long contexts. Sparse attention methods offer a…

cs.CL2024

From Persona to Personalization: A Survey on Role-Playing Language Agents

Jiangjie Chen, Xintao Wang, Rui Xu +15

Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate a…

cs.CL2024

Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs

Yifei Zhang, Xintao Wang, Jiaqing Liang +3

Large Language Models (LLMs) have exhibited impressive proficiency in various natural language processing (NLP) tasks, which involve increasingly complex reasoning. Knowledge reaso…

cs.CL2024

Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Lida Chen, Zujie Liang, Xintao Wang +7

Large language models (LLMs) have achieved great success, but their occasional content fabrication, or hallucination, limits their practical application. Hallucination arises becau…

cs.CL2024

SurveyAgent: A Conversational System for Personalized and Efficient Research Survey

Xintao Wang, Jiangjie Chen, Nianqi Li +6

In the rapidly advancing research fields such as AI, managing and staying abreast of the latest scientific literature has become a significant challenge for researchers. Although p…