Datasets:

Graph-COM
/

RAGCompass

pretty_name: RAGCompass
license: other
license_name: mixed
language:
  - en
task_categories:
  - question-answering
  - text-retrieval
tags:
  - retrieval-augmented-generation
  - retriever-routing
  - multi-hop-qa
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files: Llama-3.1-8B-Instruct_Qwen3-Embedding-0.6B/*/*.jsonl

RAGCompass data

Per-retriever results and passage corpora for the paper RAGCompass: Generalizable Retriever Routing via Reranking. Code: github.com/Graph-COM/RAGCompass.

For every query, each of five retrievers returned its top-5 passages, and a fixed reader (meta-llama/Llama-3.1-8B-Instruct) answered from them. The F1 of that answer is the utility a retriever router is trained to predict. The data covers 4 datasets x 3 splits x 5 retrievers x 5,000 queries, plus the passage corpus of every split, so routing methods can be trained and evaluated without rerunning retrieval or the reader.

Download

From the root of a clone of the code repository (*.json* fetches the data and leaves out this card):

hf download Graph-COM/RAGCompass --repo-type dataset --local-dir . --include "*.json*"

Layout

Llama-3.1-8B-Instruct_Qwen3-Embedding-0.6B/
  <dataset>_train/<retriever>.jsonl
  <dataset>_val/<retriever>.jsonl
  <dataset>/<retriever>.jsonl              # test split
dataset/
  <dataset>[_train|_val]_corpus.json       # list of {"title", "text"}

<dataset> is one of nq, musique, hotpotqa, 2wikimultihopqa. <retriever> is one of rag_1_step (dense, Qwen3-Embedding-0.6B), bm25_1_step, splade_v3_1_step, colbertv2_1_step or hipporag2-0.5_1_step (HippoRAG 2). Every JSONL file has 5,000 rows, one per query, and the five files of a split hold the same queries.

field	meaning
`id`, `question`, `answers`	query id, text, gold answers
`top_k_indices`, `top_k_scores`	the retriever's top-5 passages (indices into the split's corpus) and their scores
`pred_ans`, `raw_response`	the reader's extracted answer and full response
`f1`	F1 of `pred_ans` against `answers`
`recall`, `ndcg`, `gold_doc_list`	retrieval quality against the gold passages
`latency`	seconds for retrieval plus generation

Corpus sizes (passages):

dataset	train	val	test
nq	19,384	19,510	19,453
musique	45,240	45,195	48,236
hotpotqa	46,077	46,074	46,353
2wikimultihopqa	25,217	24,993	26,424

The questions and corpora follow the HippoRAG 2 release.

License

The data is derived from Natural Questions (CC BY-SA 3.0), HotpotQA (CC BY-SA 4.0), MuSiQue (CC BY 4.0) and 2WikiMultihopQA (Apache 2.0), with passages taken from Wikipedia (CC BY-SA). Each part is redistributed under the license of the dataset it comes from. The reader outputs were generated with Llama 3.1 and are subject to the Llama 3.1 Community License.

Citation

@misc{six2026ragcompass,
  title         = {RAGCompass: Generalizable Retriever Routing via Reranking},
  author        = {Six, Valentin and Li, Mufei and Fu, Dongqi and Xia, Yinglong and Li, Hong and Li, Pan},
  year          = {2026},
  eprint        = {2611.05596},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2611.05596}
}