1 citations · 1 across the 1 of their papers we have counts for
5 papers
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
Sara Rosenthal, Yannis Katsis, Vraj Shah +3
We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augmented generation, a popular use of large language models. We release a benchmark of 666 t…
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)…
Activated LoRA: Fine-tuned LLMs for Intrinsics
Kristjan Greenewald, Luis Lastras, Thomas Parnell +6
Low-Rank Adaptation (LoRA) has emerged as a highly efficient framework for finetuning the weights of large foundation models, and has become the go-to method for data-driven custom…
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…