FRAMES
Factuality, Retrieval, And reasoning MEasurement Set - a unified evaluation dataset of 824 challenging multi-hop questions for testing retrieval-augmented generation systems across factuality, retrieval accuracy, and reasoning capabilities, requiring integration of 2-15 Wikipedia articles per question
Kimi K2-Thinking-0905 from Moonshot AI currently leads the FRAMES leaderboard with a score of 0.870 across 2 evaluated AI models.
What FRAMES measures
FRAMES is a text benchmark that evaluates large language models on reasoning and search tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.9.
Compare leaders on the best AI for reasoning and best AI for search leaderboards.
Publication
- Paper
- Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
- Authors
- Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, and 3 others
- Published
- arXiv
- 2409.12941
Abstract
Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to understand user queries, retrieve relevant information, and synthesize coherent and accurate responses. Given the increasing real-world deployment of such systems, comprehensive evaluation becomes crucial. To this end, we propose FRAMES (Factuality, Retrieval, And reasoning MEasurement Set), a high-quality evaluation dataset designed to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. While previous work has provided datasets and benchmarks to evaluate these abilities in isolation, FRAMES offers a unified framework that provides a clearer picture of LLM performance in end-to-end RAG scenarios. Our dataset comprises challenging multi-hop questions that require the integration of information from multiple sources. We present baseline results demonstrating that even state-of-the-art LLMs struggle with this task, achieving 0.40 accuracy with no retrieval. The accuracy is significantly improved with our proposed multi-step retrieval pipeline, achieving an accuracy of 0.66 (>50% improvement). We hope our work will help bridge evaluation gaps and assist in developing more robust and capable RAG systems.
Kimi K2-Thinking-0905 leads with 87.0%, followed by 
DeepSeek-V3 at 73.3%.
Progress Over Time
Interactive timeline showing model performance evolution on FRAMES
FRAMES Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Moonshot AI | 1.0T | — | — | ||
| 2 | DeepSeek | 671B | — | — |
FAQ
Common questions about FRAMES.
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