6 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
From Features to Transformers: Redefining Ranking for Scalable Impact
Fedor Borisyuk, Lars Hertel, Ganesh Parameswaran +14
We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modifie…
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation
Hamed Firooz, Maziar Sanjabi, Adrian Englhardt +20
Ranking and recommendation systems are the foundation for numerous online experiences, ranging from search results to personalized content delivery. These systems have evolved into…