Evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems. Paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941.
Dataset Overview
824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles
Questions span diverse topics including history, sports, science, animals, health, etc.
Each question is labeled with reasoning types: numerical, tabular, multiple constraints, temporal, and post-processing
Gold answers and relevant Wikipedia articles provided for each question
Key Features
Tests end-to-end RAG capabilities in a unified framework
Requires integration of information from multiple sources
Incorporates complex reasoning and temporal disambiguation
Designed to be challenging for state-of-the-art language models
Usage
This dataset can be used to:
Evaluate RAG system performance
Benchmark language model factuality and reasoning
Develop and test multi-hop retrieval strategies
Evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems. Paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941.
Dataset Overview 824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles Questions span diverse topics including history, sports, science, animals, health, etc. Each question is labeled with reasoning types: numerical, tabular, multiple constraints, temporal, and post-processing Gold answers and relevant Wikipedia articles provided for each question
Key Features Tests end-to-end RAG capabilities in a unified framework Requires integration of information from multiple sources Incorporates complex reasoning and temporal disambiguation Designed to be challenging for state-of-the-art language models
Usage This dataset can be used to:
Evaluate RAG system performance Benchmark language model factuality and reasoning Develop and test multi-hop retrieval strategies