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cs.CL2025
Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs
Shuyuan Lin, Lei Duan, Philip Hughes +1
Conversational Information Retrieval (CIR) systems, while offering intuitive access to information, face a significant challenge: reliably handling unanswerable questions to preven…
cs.CL2025
DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing Mechanism
Dengchun Li, Naizheng Wang, Zihao Zhang +4
Instruction-based fine-tuning of large language models (LLMs) has achieved remarkable success in various natural language processing (NLP) tasks. Parameter-efficient fine-tuning (P…
cs.CL2024
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
Dengchun Li, Yingzi Ma, Naizheng Wang +8
Fine-tuning Large Language Models (LLMs) is a common practice to adapt pre-trained models for specific applications. While methods like LoRA have effectively addressed GPU memory c…