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cs.CL2026
FOCUS: Decoupling Expert Personas in LLMs to Enhance Domain Expert Capabilities
Guanyu Wang, Zidi Zhang, Xu Chu
Large Language Models (LLMs) can exhibit diverse personas, and activating expert personas has been shown to improve domain expertise and task accuracy. However, existing persona co…
cs.CL2025
GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
Hengyuan Zhang, Xinrong Chen, Yingmin Qiu +7
Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs. How…
cs.CL2025
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
Xu Chu, Zhijie Tan, Hanlin Xue +3
Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically gene…