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cs.IR2025★ 1 cited
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song +17
Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Al…
cs.CL2025★ 1 cited
AlphaPO: Reward Shape Matters for LLM Alignment
Aman Gupta, Shao Tang, Qingquan Song +10
Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and…