29 citations · 151 across the 40 of their papers we have counts for
42 papers
Granite Embedding R2 Models
Parul Awasthy, Aashka Trivedi, Yulong Li +17
We introduce the Granite Embedding R2 models, a comprehensive family of high-performance English encoder-based embedding models engineered for enterprise-scale dense retrieval appl…
Optimal Policy Minimum Bayesian Risk
Ramón Fernandez Astudillo, Md Arafat Sultan, Aashka Trivedi +4
Inference scaling helps LLMs solve complex reasoning problems through extended runtime computation. On top of long chain-of-thought (long-CoT) models, purely inference-time techniq…
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…
Graph-based Uncertainty Metrics for Long-form Language Model Outputs
Mingjian Jiang, Yangjun Ruan, Prasanna Sattigeri +2
Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities, but these systems are still known to hallucinate, and granular uncerta…
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…
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal +23
Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challengin…