Datasets:
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
scientific-papers
literature-search
arxiv
information-retrieval
sentence-transformers
hard-negatives
License:
| license: mit | |
| task_categories: | |
| - text-retrieval | |
| - sentence-similarity | |
| task_ids: | |
| - document-retrieval | |
| language: | |
| - en | |
| tags: | |
| - scientific-papers | |
| - literature-search | |
| - arxiv | |
| - information-retrieval | |
| - sentence-transformers | |
| - hard-negatives | |
| - synthetic | |
| size_categories: | |
| - 100K<n<1M | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: train.parquet | |
| - split: dev | |
| path: dev.parquet | |
| # sci_index_train | |
| A high-quality semi-synthetic training set for **embedding models for searching scientific literature**. It consists of *142,478 training* and *7,516 dev* queries over arXiv, with 631,611 query-paper positive pairs and mined hard negatives. | |
| We create this dataset to model how users actually pose queries for literature search. We mostly target descriptive information needs (*"recent advances in formal verification of stochastic dynamical systems"*) instead of titles or keywords. Many of these queries have **several** relevant papers, not one. These are the cases where general-purpose embedding models do worst. | |
| We employ this dataset to fine-tune [`granite-embedding-small-english-r2`](https://huggingface.co/ibm-granite/granite-embedding-small-english-r2) in our [**`scientific_granite`**](https://huggingface.co/daniel-gomm/scientific_granite) model. On the held-out benchmark [**`sci_index_bench`**](https://huggingface.co/datasets/daniel-gomm/sci_index_bench) it improves ndcg@10 from 0.756 to **0.800**, with **+0.082** on exploratory queries, showing the value of this dataset for fine-tuning embedding models for literature search. The gains also generalize to other benchmarks with gains in ndgc@10 of **+0.057** on [LitSearch](https://aclanthology.org/2024.emnlp-main.840/) over the base model. | |
| ## Dataset composition | |
| We synthetically sample different types of input queries. | |
| | category | rows | row % | positive pairs | pair % | pos/row | neg/row | median words | of which jargon variants | | |
| |---|---:|---:|---:|---:|---:|---:|---:|---:| | |
| | `nl_question` | 48,864 | 34.3% | 48,864 | 7.7% | 1.0 | 14.5 | 13 | 6,477 | | |
| | `exploratory` | 39,103 | 27.4% | 389,344 | 61.6% | 10.0 | 90.5 | 12 | 7,056 | | |
| | `problem` | 27,172 | 19.1% | 27,172 | 4.3% | 1.0 | 14.3 | 8 | 2,759 | | |
| | `cit_rewrite` | 15,579 | 10.9% | 154,471 | 24.5% | 9.9 | 89.2 | 21 | 105 | | |
| | `known_item` | 5,994 | 4.2% | 5,994 | 0.9% | 1.0 | 14.7 | 12 | 966 | | |
| | `keyword` | 5,766 | 4.0% | 5,766 | 0.9% | 1.0 | 8.3 | 5 | 1,640 | | |
| | **train total** | **142,478** | | **631,611** | | | | | **19,003** | | |
| `dev`: 7,516 queries in the same proportions (nl_question 2,606 · exploratory 2,074 · | |
| problem 1,442 · cit_rewrite 751 · known_item 330 · keyword 313). | |
| These categories simulate the following: | |
| | category | what it simulates | example | | |
| |---|---|---| | |
| | `exploratory` | a survey-style topic need with several relevant papers | *current limitations and scaling challenges in high-density associative memory architectures* | | |
| | `cit_rewrite` | a specific research question, phrased the way other papers describe the cited work | *How do latent diffusion models utilize a gradual spreading of data distribution to perform short-term rainfall prediction?* | | |
| | `nl_question` | a natural-language question about one paper's finding | *Can federated learning match centralized training on generalizing to unseen mammographic data?* | | |
| | `problem` | a terse, search-engine-style description of a problem or method | *domain expert annotated dataset for medical summarization* | | |
| | `keyword` | keyword fragments | *improved sugama collision* | | |
| | `known_item` | a half-remembered paper: partial or garbled title, often with an author | *a paper by Antamanova, something about Anode current saturation of* | | |
| - **Jargon variants** (13.3% of rows, `generator` starting with `jargon_`) are copies of | |
| another query in the same category with a technical term swapped for its acronym, its | |
| expansion or a synonym. They are paired with the original query and share its labels, | |
| which teaches invariance to how a concept is named. Either a curated acronym lexicon | |
| (`jargon_lexicon`) or an LLM (`jargon_llm`) made the substitution. | |
| - **`cit_rewrite`** queries are LLM paraphrases, written as questions, of sentences in | |
| which other arXiv papers cite the target paper. The sentences were retrieved via the | |
| [Semantic Scholar API](https://www.semanticscholar.org/product/api). **No citation | |
| sentence is included verbatim.** | |
| ## Schema | |
| | column | type | notes | | |
| |---|---|---| | |
| | `query_id` | string | content hash of the query | | |
| | `category` | string | query type (table above) | | |
| | `generator` | string | specific generator; `jargon_*` marks a jargon variant | | |
| | `model_tier` | string | model (or `algorithmic`) that produced `q` | | |
| | `register` | string | `question` · `brief` · `keyword` · `statement` · `recollection` | | |
| | `specificity` | string | `broad` · `specific` | | |
| | `paper_id` | string | arXiv ID of the paper the query was generated from | | |
| | `q` | string | **the query text** | | |
| | `positives` | list[string] | arXiv IDs of relevant papers, sorted by `positive_scores` (descending) | | |
| | `positive_scores` | list[float] | cross-encoder relevance in [0, 1], aligned to `positives` | | |
| | `hard_negatives` | list[string] | arXiv IDs of mined non-relevant papers | | |
| | `no_in_batch_negatives` | bool | true for multi-positive rows. Mask their positives from each other | | |
| | `dev_hard` | bool | `dev` only: the lowest-lexical-overlap tercile (2,929 rows) | | |
| **Single- vs multi-positive rows.** For the four single-positive categories, | |
| `positives == [paper_id]`. For `exploratory` and `cit_rewrite`, `positives` is the | |
| **top 10 candidates by cross-encoder score**, whether or not they include the source paper. The | |
| source paper is in the set for 40% (`exploratory`) and 67% (`cit_rewrite`) of rows, because | |
| another paper often answers the query better. Treat `positive_scores` as soft labels. | |
| The lowest-ranked positives can score close to 0, so a hard 0/1 reading over-trusts the tail. | |
| Positives and negatives are **arXiv identifiers, not text**. A script that rebuilds the | |
| paper text from the public arXiv metadata snapshot ships with the dataset | |
| ([Getting the paper text](#getting-the-paper-text)). | |
| ## Usage | |
| We only include the arXiv identifiers with this dataset since not all papers on arXiv come with sufficiently permissive licenses. To use the dataset, one thus has to re-recreate the corpus from the [kaggle snapshot of arXiv metadata](https://www.kaggle.com/datasets/Cornell-University/arxiv). Download this snapshot, then you can use the `rebuild_corpus.py` script to recreate the required corpus: | |
| ```bash | |
| pip install pyarrow | |
| python rebuild_corpus.py --snapshot arxiv-metadata-oai-snapshot.json \ | |
| --ids . --verify doc_checksums.parquet --out corpus.parquet | |
| ``` | |
| ## ⚠️ Important Notes | |
| 1. **Row share is not signal share.** `exploratory` and `cit_rewrite` rows carry ~10 graded positives and ~90 hard negatives each. The other four categories carry one positive and at most 15 negatives. `exploratory` is 27% of rows but **62% of positive pairs**. If you flatten rows to (query, positive) pairs, the mix you train on is not the mix in the row counts. | |
| 2. **Mask positives out of in-batch negatives.** Rows with `no_in_batch_negatives = true` (all `exploratory` and `cit_rewrite` rows) have several genuine positives. If two of a query's positives land in the same batch, standard MNRL will push them apart. | |
| 3. **Don't tune on `dev` and then report `sci_index_bench`.** All 6,825 benchmark queries come from this dataset's `dev` split. Use the benchmark's `selection` split for checkpoint selection and report its `test` split. `dev` here suits in-training monitoring. | |
| ## How the dataset was built | |
| 1. **Corpus.** All arXiv papers in the July 2026 Kaggle metadata snapshot with a title | |
| and an abstract of at least 200 characters, deduplicated by normalised title: | |
| 3,040,442 papers. | |
| 2. **Source papers.** A stratified sample of source papers. Papers from the | |
| evaluation benchmarks below were excluded from the sample. | |
| 3. **Query generation.** Per category, LLM or algorithmic (tables above). The LLM | |
| prompts (shared by every LLM category, including the Gemini one) ask for a search need | |
| rather than a summary of the paper. They forbid naming the paper or its authors, never | |
| reuse the title as a phrase, and copy at most 4 consecutive words from the title or | |
| abstract. | |
| 4. **Quality control.** Schema validation, exact-normalised deduplication across all | |
| categories, fastText language ID, and a check that no `keyword` / `known_item` query | |
| is simply the paper's title. All categories except `exploratory` also pass a | |
| round-trip filter, which keeps a query only if a frozen | |
| [`granite-embedding-311m-multilingual-r2`](https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2) | |
| ranks its source paper in the top 100 of the full corpus. | |
| 5. **Hard-negative mining.** Candidates are the union of BM25 and dense retrieval (top | |
| 100 each) over the full corpus. | |
| - **Single-positive rows:** [NV-Retriever](https://arxiv.org/abs/2407.15831) | |
| TopK-PercPos drops any candidate scoring ≥ 95% of the positive. A | |
| [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) | |
| cross-encoder then drops any negative scoring within 0.05 of the positive. | |
| At most 15 negatives are kept. | |
| - **Multi-positive rows:** the cross-encoder scores every candidate. The top 10 | |
| become graded positives, candidates below the median score become negatives, and | |
| the ambiguous middle band is discarded. | |
| 6. **Packaging.** Rows left with no negatives are dropped. For categories over quota, | |
| the 5% tails of the positive–negative cross-encoder margin are trimmed before random | |
| downsampling. `exploratory` has no single positive to take a margin against, so it | |
| is only downsampled. The train/dev split is by source paper (5% dev). | |
| 7. **Evaluation-set decontamination.** Every paper in the | |
| [LitSearch](https://huggingface.co/datasets/princeton-nlp/LitSearch) and | |
| [DORIS-MAE](https://zenodo.org/records/8299749) corpora that could be matched to arXiv | |
| (by ID for DORIS-MAE, by normalised title for LitSearch) is removed from every role: | |
| source papers, positives and hard negatives. That is 20,898 papers in total, and it | |
| removed 3,005 positives and 46,218 negatives from training rows. Six rows were left | |
| with no negatives and dropped. The released data references **none** of the 20,898 | |
| papers (verified). | |
| ## Limitations | |
| - **Synthetic queries.** All queries are LLM-generated or algorithmically constructed, not sampled from search logs. They are designed to resemble literature-search queries and are not evidence of what people actually ask. The LitSearch and DORIS-MAE results are evidence that training carries over to human-written queries but we expect possible gains from actual human logs. | |
| - **Uneven query-shape coverage.** `keyword` + `known_item` are 8.2% of rows, and title-as-query is absent. Models trained on this data regress on title lookup and keyword-style benchmarks. | |
| - **Uneven negative depth.** Multi-positive rows average ~90 negatives. The rest are capped at 15, and `keyword` averages 8.3 (37.5% of `keyword` rows have fewer than 5). | |
| - **The train/dev split is by source paper only.** A paper can still appear as a positive on both sides: 32.7% of papers that are `dev` positives are also `train` positives. No query text or source paper is shared. `sci_index_bench` removes this overlap from its relevance labels. | |
| - **Labels come from models.** Multi-positive sets and negative filtering rely on a cross-encoder. There are no human relevance judgements. | |
| - **Deduplication is exact-normalised only.** There is no near-duplicate pass. Jargon variants are near-duplicates by design. | |
| - **English and arXiv only.** Physics, mathematics and computer science are well covered. Biomedicine, chemistry and the social sciences are thin. | |
| - **No test split.** Report on [`sci_index_bench`](https://huggingface.co/datasets/daniel-gomm/sci_index_bench) `test` or an external benchmark. | |
| ## Licence | |
| [MIT](https://opensource.org/license/MIT). The dataset contains generated query text and arXiv identifiers. No paper titles, abstracts or full texts are redistributed. The arXiv metadata compilation it refers to is CC0. | |