2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.HC2026★ 2 cited
A Survey of LLM Alignment: Instruction Understanding, Intention Reasoning, and Reliable Generation
Zongyu Chang, Feihong Lu, Ziqin Zhu +10
Large language models have demonstrated exceptional capabilities in understanding and generation. However, in real-world scenarios, users' natural language expressions are often in…
cs.CL2025
Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation
Qianren Mao, Qili Zhang, Hanwen Hao +11
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution for enhancing the accuracy and credibility of Large Language Models (LLMs), particularly in Questi…
cs.CL2025
Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss
Zhuoang Cai, Zhenghao Li, Yang Liu +2
Classification tasks often suffer from imbal- anced data distribution, which presents chal- lenges in food hazard detection due to severe class imbalances, short and unstructured t…