license: apache-2.0
FRAMES: Factuality, Retrieval, And reasoning MEasurement Set
FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.
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
Baseline Results
We provide baseline results using state-of-the-art models like Gemini-Pro-1.5-0514:
- Naive prompting: 40.8% accuracy
- BM25 retrieval (4 docs): 47.4% accuracy
- Oracle retrieval: 72.9% accuracy
- Multi-step retrieval & reasoning: 66% accuracy
Citation
If you use this dataset in your research, please cite our paper:
@misc{krishna2024factfetchreasonunified, title={Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation}, author={Satyapriya Krishna and Kalpesh Krishna and Anhad Mohananey and Steven Schwarcz and Adam Stambler and Shyam Upadhyay and Manaal Faruqui}, year={2024}, eprint={2409.12941}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2409.12941}, }
We hope FRAMES will be useful for advancing RAG systems and language model capabilities. For more details, please refer to our full paper.