15 citations · 40 across the 9 of their papers we have counts for
3 papers · 1 filter
Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges
Nandan Thakur, Ronak Pradeep, Shivani Upadhyay +3
Retrieval-augmented generation (RAG) enables large language models (LLMs) to generate answers with citations from source documents containing "ground truth", thereby reducing syste…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems
Nandan Thakur, Suleman Kazi, Ge Luo +2
Traditional retrieval-augmented generation (RAG) benchmarks evaluate systems using heuristic-based metrics, but these require human preferences as the ground truth for reference. I…