---
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.