9 citations · 21 across the 7 of their papers we have counts for
3 papers · 1 filter
Overview of the TREC 2023 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +5
This is the fifth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human-annotated training labels a…
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…
The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models
Ronak Pradeep, Nandan Thakur, Shivani Upadhyay +3
Large Language Models (LLMs) have significantly enhanced the capabilities of information access systems, especially with retrieval-augmented generation (RAG). Nevertheless, the eva…