| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | tags: |
| | - rag |
| | - long-context |
| | - llm-search |
| | - reasoning |
| | - factuality |
| | - retrieval |
| | - question-answering |
| | - iterative-search |
| | task_categories: |
| | - text-classification |
| | - token-classification |
| | - table-question-answering |
| | - question-answering |
| | pretty_name: Who are I or you |
| | size_categories: |
| | - n>1T |
| | --- |
| | |
| | # 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. |
| | Our paper with details and experiments is available on arXiv: [https://arxiv.org/abs/2409.12941](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 |
| |
|
| | ## 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. |
