206 citations · 277 across the 18 of their papers we have counts for
4 papers · 1 filter
A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis +2
Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs)…
RAGAPHENE: A RAG Annotation Platform with Human Enhancements and Edits
Kshitij Fadnis, Sara Rosenthal, Maeda Hanafi +2
Retrieval Augmented Generation (RAG) is an important aspect of conversing with Large Language Models (LLMs) when factually correct information is important. LLMs may provide answer…
A Library of LLM Intrinsics for Retrieval-Augmented Generation
Marina Danilevsky, Kristjan Greenewald, Chulaka Gunasekara +13
In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for t…
MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
Yannis Katsis, Sara Rosenthal, Kshitij Fadnis +7
Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is…