metadata
dataset_info:
features:
- name: source
dtype: string
- name: query_id
dtype: string
- name: query
dtype: string
- name: doc_name
dtype: string
- name: answer
sequence: string
- name: doc_url
dtype: string
- name: num_doc_labels
dtype: int32
- name: doc_pool
sequence:
- name: mapped_id
dtype: string
- name: doc_name
dtype: string
- name: doc_chunk
dtype: string
- name: support
dtype: int32
- name: oracle
struct:
- name: mapped_id
dtype: string
- name: doc_name
dtype: string
- name: doc_chunk
dtype: string
- name: support
dtype: int32
splits:
- name: train
num_bytes: 59957279
num_examples: 7560
download_size: 35533441
dataset_size: 59957279
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: mirage
size_categories:
- 1K<n<10K
MIRAGE dataset
MIRAGE is a benchmark dataset for evaluating Retrieval-Augmented Generation (RAG) systems, featuring 7,560 QA pairs and 37,800 context pools curated from diverse Wikipedia-based QA datasets (IfQA, NaturalQA, TriviaQA, DROP, PopQA). MIRAGE enables robust assessment of LLMs and retrievers under realistic, noisy, and oracle settings, and introduces novel metrics for analyzing context sensitivity, noise vulnerability, and retrieval effectiveness.
You can find our paper and github repository below:
