5 papers · 1 filter
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…
Weak Supervision for Improved Precision in Search Systems
Sriram Vasudevan
Labeled datasets are essential for modern search engines, which increasingly rely on supervised learning methods like Learning to Rank and massive amounts of data to power deep lea…
Learning to Retrieve for Job Matching
Jianqiang Shen, Yuchin Juan, Shaobo Zhang +21
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often in…