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| license: apache-2.0 |
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| # FRAMES: Factuality, Retrieval, And reasoning MEasurement Set |
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| FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning. |
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| ## Dataset Overview |
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| - 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 |
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| ## Key Features |
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| - 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 |
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| ## Usage |
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| This dataset can be used to: |
| - Evaluate RAG system performance |
| - Benchmark language model factuality and reasoning |
| - Develop and test multi-hop retrieval strategies |
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| ## Baseline Results |
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| We provide baseline results using state-of-the-art models like Gemini-Pro-1.5-0514: |
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| - Naive prompting: 40.8% accuracy |
| - BM25 retrieval (4 docs): 47.4% accuracy |
| - Oracle retrieval: 72.9% accuracy |
| - Multi-step retrieval & reasoning: 66% accuracy |
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| ## Citation |
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| If you use this dataset in your research, please cite our paper: |
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| ``` |
| @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}, |
| } |
| ``` |
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| We hope FRAMES will be useful for advancing RAG systems and language model capabilities. For more details, please refer to our full paper. |
