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cs.IR2025
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…
cs.IR2024
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…
cs.IR2024
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…