4 papers
From Generation to Scoring: Scaling LLM-Based Semantic Labeling at LinkedIn
Jian Sheng, Sriram Vasudevan, Sundara Raman Ramachandran +12
For efficiency, industrial large language model (LLM) labelers often pack many candidate labels into one prompt and generate a YES/NO answer for each, even when each decision depen…
Semantic Search At LinkedIn
Fedor Borisyuk, Sriram Vasudevan, Muchen Wu +71
Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present…
MixLM: High-Throughput and Effective LLM Ranking via Text-Embedding Mix-Interaction
Guoyao Li, Ran He, Shusen Jing +21
Large language models (LLMs) excel at capturing semantic nuances and therefore show impressive relevance ranking performance in modern recommendation and search systems. However, t…
Scaling Up Efficient Small Language Models Serving and Deployment for Semantic Job Search
Kayhan Behdin, Qingquan Song, Sriram Vasudevan +17
Large Language Models (LLMs) have demonstrated impressive quality when applied to predictive tasks such as relevance ranking and semantic search. However, deployment of such LLMs r…