README.md

metadata

license: cc-by-4.0
language:
  - en
task_categories:
  - question-answering
pretty_name: SARA QASPER (reformatted)
tags:
  - retrieval-augmented-generation
  - context-compression
  - scientific-papers
configs:
  - config_name: qa
    default: true
    data_files:
      - split: train
        path: QASPER_train.jsonl
      - split: test
        path: QASPER_test.jsonl
  - config_name: compression_alignment
    data_files:
      - split: train
        path: QASPER_compression_alignment_train.jsonl
      - split: validation
        path: QASPER_compression_alignment_dev.jsonl

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 = 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

@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}
}