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cs.CL2025
"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…
cs.CL2024
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…
cs.CL2024
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…