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SharkSpicy/wikidata

SR-KI Dataset The dataset accompanying SR-KI: Scalable and Real-Time Knowledge Integration into LLMs via Supervised Attention (AAAI 2026). Overview The SR-KI Dataset provides Chinese question-answering data for training and evaluating the supervised-attention knowledge-integration method introduced in the SR-KI paper. Each example pairs a question and answer with the supporting knowledge and its corresponding material identifier, enabling models to produce… See the full description on the dataset page: https://huggingface.co/datasets/SharkSpicy/wikidata.

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<h1 align="center">SR-KI Dataset</h1>

<p align="center"> <a href="https://arxiv.org/abs/2511.06446"><img src="https://img.shields.io/badge/arXiv-2511.06446-b31b1b.svg" alt="arXiv"></a> <a href="https://github.com/SharkSpicy-NLP/SR-KI"><img src="https://img.shields.io/badge/GitHub-SR--KI-181717.svg?logo=github" alt="GitHub"></a> </p>

<p align="center"> The dataset accompanying <em>SR-KI: Scalable and Real-Time Knowledge Integration into LLMs via Supervised Attention</em> (AAAI 2026). </p>

Overview

The SR-KI Dataset provides Chinese question-answering data for training and evaluating the supervised-attention knowledge-integration method introduced in the SR-KI paper. Each example pairs a question and answer with the supporting knowledge and its corresponding material identifier, enabling models to produce grounded answers with explicit source attribution.

For details about the method, experimental setup, and implementation, please refer to the paper and the official GitHub repository.

Dataset Structure

The dataset contains train and test splits. Each record includes the following fields:

FieldDescription
doc_idIdentifier(s) of the supporting material.
nodesKnowledge elements associated with the example.
QOriginal question.
AReference answer.
kb_infoStructured supporting knowledge and material metadata.
qa_typeQuestion-answering category.
Q_formatFormatted model input or instruction.
A_formatFormatted target answer with source attribution.

Citation

If you find this dataset helpful in your research, we would kindly appreciate a citation:

bibtex
@misc{yu2025srkiscalablerealtimeknowledge,
  title         = {SR-KI: Scalable and Real-Time Knowledge Integration into LLMs via Supervised Attention},
  author        = {Bohan Yu and Wei Huang and Kang Liu},
  year          = {2025},
  eprint        = {2511.06446},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2511.06446}
}