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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- rag |
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- long-context |
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- llm-search |
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- reasoning |
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- factuality |
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- retreival |
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- question-answering |
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- iterative-search |
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--- |
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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 |
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- Questions span diverse topics including history, sports, science, animals, health, etc. |
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- Each question is labeled with reasoning types: numerical, tabular, multiple constraints, temporal, and post-processing |
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- 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 |
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- Requires integration of information from multiple sources |
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- Incorporates complex reasoning and temporal disambiguation |
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- 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: |
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- Evaluate RAG system performance |
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- Benchmark language model factuality and reasoning |
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- 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 |
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- BM25 retrieval (4 docs): 47.4% accuracy |
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- Oracle retrieval: 72.9% accuracy |
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- 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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``` |
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@inproceedings{Krishna2024FactFA, |
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title={Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation}, |
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author={Satyapriya Krishna and Kalpesh Krishna and Anhad Mohananey and Steven Schwarcz and Adam Stambler and Shyam Upadhyay and Manaal Faruqui}, |
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year={2024}, |
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url={https://api.semanticscholar.org/CorpusID:272753636} |
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} |
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``` |
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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. |
