4 citations · 6 across the 4 of their papers we have counts for
4 papers
RankLLM: A Python Package for Reranking with LLMs
Sahel Sharifymoghaddam, Ronak Pradeep, Andre Slavescu +7
The adoption of large language models (LLMs) as rerankers in multi-stage retrieval systems has gained significant traction in academia and industry. These models refine a candidate…
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
Jasper Xian, Saron Samuel, Faraz Khoubsirat +7
We develop a method for training small-scale (under 100M parameter) neural information retrieval models with as few as 10 gold relevance labels. The method depends on generating sy…
Can't Hide Behind the API: Stealing Black-Box Commercial Embedding Models
Manveer Singh Tamber, Jasper Xian, Jimmy Lin
Embedding models that generate dense vector representations of text are widely used and hold significant commercial value. Companies such as OpenAI and Cohere offer proprietary emb…
Vector Search with OpenAI Embeddings: Lucene Is All You Need
Jimmy Lin, Ronak Pradeep, Tommaso Teofili +1
We provide a reproducible, end-to-end demonstration of vector search with OpenAI embeddings using Lucene on the popular MS MARCO passage ranking test collection. The main goal of o…