3 papers
cs.IR2025
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
BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation
Fengyi Li, Kayhan Behdin, Natesh Pillai +3
Text segmentation based on the semantic meaning of sentences is a fundamental task with broad utility in many downstream applications. In this paper, we propose a graphical model-b…
cs.CL2025
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…