Sara Rosenthal, Yannis Katsis, Vraj Shah, Lihong He, Lucian Popa, Marina Danilevsky. Findings of the Association for Computational Linguistics: ACL 2026. 2026.

Retrieval Augmented Generation (RAG) is an important aspect of conversing with Large Language Models (LLMs) when factually correct information is important. LLMs may provide answers that appear correc...

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 asked to generate a ...

Large Language Models (LLM) have become a popular approach for implementing Retrieval Augmented Generation (RAG) systems, and a significant amount of effort has been spent on building good models and ...

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IBM’s new benchmark evaluates LLMs on interactive question-answering tasks using

Multi-Turn RAG Benchmark. Contribute to IBM/mt-rag-benchmark development by creating an account on GitHub.

The repository contains generative AI analytics platform application code. - IBM/InspectorRAGet

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