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cs.CL2025
Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
Hao Lin, Peitong Xie, Jingxue Chen +3
Retrieval-Augmented Generation (RAG) systems rely heavily on the retrieval stage, particularly the coarse-ranking process. Existing coarse-ranking optimization approaches often str…
cs.CL2025
No Query, No Access
Wenqiang Wang, Siyuan Liang, Yangshijie Zhang +3
Textual adversarial attacks mislead NLP models, including Large Language Models (LLMs), by subtly modifying text. While effective, existing attacks often require knowledge of the v…
cs.CL2024
Designing Domain-Specific Large Language Models: The Critical Role of Fine-Tuning in Public Opinion Simulation
Haocheng Lin
Large language models (LLMs) have transformed natural language processing, yet face challenges in specialized tasks such as simulating opinions on environmental policies. This pape…