Update README.md (#1)

- Update README.md (3d6574dad48c3add16d77c0423777e5e28ca3226)


Co-authored-by: Satya <skrishna@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,45 @@
----
-license: apache-2.0
----
+---
+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:
+
+
+
+We hope FRAMES will be useful for advancing RAG systems and language model capabilities. For more details, please refer to our full paper.