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Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation
Wei-Rui Chen, Vignesh Kothapalli, Ata Fatahibaarzi +5
Distilling the capabilities from a large reasoning model (LRM) to a smaller student model often involves training on substantial amounts of reasoning data. However, knowledge disti…
LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation
Zhoutong Fu, Yihan Cao, Yi-Lin Chen +16
Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applica…
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…