| --- |
| license: odc-by |
| configs: |
| - config_name: address |
| data_files: |
| - {split: train, path: data/address/train.parquet} |
| - {split: validation, path: data/address/validation.parquet} |
| - {split: test, path: data/address/test.parquet} |
| - {split: silver, path: data/address/silver.parquet} |
| - config_name: broaden |
| data_files: |
| - {split: train, path: data/broaden/train.parquet} |
| - {split: validation, path: data/broaden/validation.parquet} |
| - {split: test, path: data/broaden/test.parquet} |
| - {split: silver, path: data/broaden/silver.parquet} |
| - config_name: specify |
| data_files: |
| - {split: train, path: data/specify/train.parquet} |
| - {split: validation, path: data/specify/validation.parquet} |
| - {split: test, path: data/specify/test.parquet} |
| - {split: silver, path: data/specify/silver.parquet} |
| - config_name: candidates |
| data_files: |
| - {split: train, path: data/candidates/train.parquet} |
| - {split: validation, path: data/candidates/validation.parquet} |
| - {split: test, path: data/candidates/test.parquet} |
| --- |
| |
| # RATIO: Retrieval Across Typed Ideation Operations |
|
|
| A benchmark for retrieving scientific ideation moves: given a research statement |
| (query sentence), retrieve sentences from other papers that **Address**, |
| **Broaden**, or **Specify** it. Built from full-text computer-science papers. |
|
|
| ## Structure |
|
|
| ``` |
| data/ |
| address/ train.parquet validation.parquet test.parquet silver.parquet # query-gold pairs + judge-validated test subset |
| broaden/ train.parquet validation.parquet test.parquet silver.parquet # query-gold pairs + judge-validated test subset |
| specify/ train.parquet validation.parquet test.parquet silver.parquet # query-gold pairs + judge-validated test subset |
| candidates/ train.parquet validation.parquet test.parquet # shared retrieval corpus |
| ``` |
|
|
| - Each relation config contains query-gold sentence pairs; every query has exactly one gold. |
| - `candidates` is the retrieval corpus per split (golds + distractors), shared across all three relations. |
| - `silver` is the subset of the test set whose (query, gold) pair was validated by an LLM judge under two independent prompts (both must accept). Silver retrieval uses the test candidates corpus. |
| - Splits are temporal: train 2015-Sep 2025, validation Q4 2025, test 2026 onward. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| pairs = load_dataset("maayans/RATIO", "address") # train / validation / test / silver |
| corpus = load_dataset("maayans/RATIO", "candidates") # retrieval corpus (train / validation / test) |
| ``` |
|
|
| Retrieval for a given split is performed against the `candidates` corpus of that same split; |
| the silver subset shares the test candidates. |
|
|
| ## Models |
|
|
| Nine fine-tuned bi-encoders accompany the benchmark - three encoders x three relations, |
| named `maayans/<base-model>__ratio-<relation>` with relation in `address` / `broaden` / `specify` |
| (e.g. [`maayans/modernbert-embed-large__ratio-address`](https://huggingface.co/maayans/modernbert-embed-large__ratio-address)), |
| all grouped in the [RATIO collection](https://huggingface.co/collections/maayans/ratio). |
|
|
| | Base model | When to use it | Query prefix | Candidate prefix | |
| |---|---|---|---| |
| | `modernbert-embed-large` | **Top choice** - best overall retrieval quality | `search_query: ` | `search_document: ` | |
| | `stella_en_1.5B_v5` | Very close performance; preferred for longer texts | built-in `s2p_query` prompt | none | |
| | `all-mpnet-base-v2` | When a light and fast model is needed | `query: ` | `document: ` | |
|
|
| Prefixes must be applied at inference exactly as in training: |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| m = SentenceTransformer("maayans/modernbert-embed-large__ratio-address") |
| q = m.encode(["search_query: " + s for s in queries]) |
| c = m.encode(["search_document: " + s for s in candidates]) |
| |
| # stella: built-in prompt on queries only, candidates unprefixed |
| m = SentenceTransformer("maayans/stella_en_1.5B_v5__ratio-address", trust_remote_code=True) |
| q = m.encode(queries, prompt_name="s2p_query") |
| c = m.encode(candidates) |
| ``` |
|
|
| ## Citation |
| ``` |
| @misc{sharon2026ratiobenchmarkretrievaltyped, |
| title={RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature}, |
| author={Maayan Sharon and Tom Hope}, |
| year={2026}, |
| eprint={2608.27394}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2608.27394}, |
| } |
| ``` |
