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cs.CL2025
Do Retrieval Augmented Language Models Know When They Don't Know?
Youchao Zhou, Heyan Huang, Yicheng Liu +5
Existing large language models (LLMs) occasionally generate plausible yet factually incorrect responses, known as hallucinations. Two main approaches have been proposed to mitigate…
cs.CL2025
InComeS: Integrating Compression and Selection Mechanisms into LLMs for Efficient Model Editing
Shuaiyi Li, Zhisong Zhang, Yang Deng +6
Although existing model editing methods perform well in recalling exact edit facts, they often struggle in complex scenarios that require deeper semantic understanding rather than…
cs.CL2025★ 3 cited
From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations
Shenghan Wu, Yimo Zhu, Wynne Hsu +2
The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and…