29 citations · 151 across the 40 of their papers we have counts for
5 papers · 1 filter
Granite Embedding Models
Parul Awasthy, Aashka Trivedi, Yulong Li +19
We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, wit…
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support
Paulina Toro Isaza, Michael Nidd, Noah Zheutlin +7
Clients wishing to implement generative AI in the domain of IT Support and AIOps face two critical issues: domain coverage and model size constraints due to model choice limitation…
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…
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
Jon Saad-Falcon, Omar Khattab, Keshav Santhanam +6
Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can dim…
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Keshav Santhanam, Jon Saad-Falcon, Martin Franz +7
Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks. Unfortunately, some di…