| --- |
| 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*](https://arxiv.org/abs/2611.05596). Code: [github.com/Graph-COM/RAGCompass](https://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): |
|
|
| ```bash |
| 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](https://huggingface.co/datasets/osunlp/HippoRAG_2). |
|
|
| ## 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](https://www.llama.com/llama3_1/license/). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
