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cs.LG2026
MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI
Bohan Lyu, Yucheng Yang, Siqiao Huang +25
Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities i…
cs.LG2026
Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG
Yicheng Zhang, Zhen Qin, Zhaomin Wu +2
Retrieval-augmented generation (RAG) enables large language models (LLMs) to produce evidence-based responses, and its performance hinges on the matching between the retriever and…
cs.LG2024
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures
Yicheng Zhang, Zhen Qin, Zhaomin Wu +2
Large language models (LLMs) are increasingly powering web-based applications, whose effectiveness relies on fine-tuning with large-scale instruction data. However, such data often…