Datasetpublic

Ahren09/SARA-QASPER

SARA QASPER (reformatted) Reformatted QASPER data used by SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression (ACL 2026, arXiv:2507.05633). Code: Ahren09/SARA. The SARA Quick Start (python -m src.data.make_qasper_splits) downloads this dataset automatically; you can also load it directly: from datasets import load_dataset qa = load_dataset("Ahren09/SARA-QASPER", "qa") # train / test align =… See the full description on the dataset page: https://huggingface.co/datasets/Ahren09/SARA-QASPER.

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Dataset Card

SARA QASPER (reformatted)

Reformatted QASPER data used by SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression (ACL 2026, arXiv:2507.05633). Code: Ahren09/SARA.

The SARA Quick Start (python -m src.data.make_qasper_splits) downloads this dataset automatically; you can also load it directly:

python

from datasets import load_dataset

qa = load_dataset("Ahren09/SARA-QASPER", "qa")                          # train / test
align = load_dataset("Ahren09/SARA-QASPER", "compression_alignment")    # train / validation

Configs

`qa` (default)

One record per answerable QASPER question. train (2,321 rows over the official train papers) and test (1,312 rows, official test papers).

Field	Type	Description
`id`	str	Running row index.
`example_id`	str	Paper index — used for leakage-safe document-level train/dev splitting.
`question`	str	The QASPER question.
`context`	list[str]	BM25-ranked paper contexts, each formatted `"Section name\t<text>"`. Retrieval in SARA is built in-memory from this field; no separate index is needed.
`answer`	str	Short gold answer (a span, value, phrase, or yes/no) derived from the official QASPER annotations. Used as both the training target and the evaluation reference.
`choices`	null	Unused for QASPER (kept for schema compatibility with multiple-choice datasets).
`question_type`	str	One of `extractive`, `free_form`, `yes_no`, `unanswerable`.

Models trained on this data answer with chain-of-thought followed by the final short answer wrapped in tags: ... reasoning ... <answer>short answer</answer>; evaluation extracts the tagged span and scores it against answer.

`compression_alignment`

Text snippets from QASPER paper bodies used for the SARA projector-alignment warm-up (the projector learns to reconstruct a document from its semantic compression vector before QA fine-tuning). train (22,111 rows) and validation (200 rows); each record is {"text": "<document text snippet>"}.

Provenance

—Derived from `allenai/qasper` (Dasigi et al., NAACL 2021), released under CC BY 4.0. This derivative is released under the same license with attribution.
—context was built from each paper's title/abstract/sections and BM25-ranked per question; BIBREF citation markers were stripped.
—Earlier revisions of this dataset carried an additional LLM-rewritten answer_reformatted field; it has been removed — the short gold answer is the single reference. The old revision remains available in this repo's git history.

Checksums (sha256)

0ec3d1bbab2f85341a9432b263ca90669f5cfd45bb2163170cedddf8ff60742c  QASPER_train.jsonl
a26afe8a11e350e5a860c83b75ee4938b2b89f19dc4883cea063dd90642bc1e1  QASPER_test.jsonl
1d6386a84408127a20f01db92f8b9a73ec9499d884d0cab3c2c9f07c4494aa12  QASPER_compression_alignment_train.jsonl
9de81c3cbf42aa4b4001a57762c37f981eb2ae0c5b468c3bfebffe65438f8a0f  QASPER_compression_alignment_dev.jsonl

Citation

bibtex

@inproceedings{jin2025sara,
  title={SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression},
  author={Jin, Yiqiao and Sharma, Kartik and Rakesh, Vineeth and Dou, Yingtong and Pan, Menghai and Das, Mahashweta and Kumar, Srijan},
  booktitle={ACL},
  year={2026}
}

@inproceedings{dasigi2021dataset,
  title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
  author={Dasigi, Pradeep and Lo, Kyle and Beltagy, Iz and Cohan, Arman and Smith, Noah A. and Gardner, Matt},
  booktitle={NAACL},
  year={2021}
}