4 citations · 4 across the 3 of their papers we have counts for
4 papers
"Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models
Yufei Tao, Adam Hiatt, Rahul Seetharaman +1
Large language models are capable of leveraging both contextual and parametric knowledge but how they prioritize and integrate these sources remains underexplored. We introduce CoP…
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis
Reshmi Ghosh, Rahul Seetharaman, Hitesh Wadhwa +6
Retrieval Augmented Generation (RAG) is a widely used approach for leveraging external context in several natural language applications such as question answering and information r…
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Hitesh Wadhwa, Rahul Seetharaman, Somyaa Aggarwal +6
Retrieval Augmented Generation (RAG) enriches the ability of language models to reason using external context to augment responses for a given user prompt. This approach has risen…
Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads
Dharma Shukla, Muthian Sivathanu, Srinidhi Viswanatha +23
Lowering costs by driving high utilization across deep learning workloads is a crucial lever for cloud providers. We present Singularity, Microsoft's globally distributed schedulin…