language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
SPARK: Adaptive Parametric Knowledge Participation for Faithful Retrieval-Augmented Generation
This repository contains the model checkpoints for SPARK, a lightweight framework designed to resolve knowledge conflicts in retrieval-augmented generation (RAG).
- Paper: SPARK: Adaptive Parametric Knowledge Participation for Faithful Retrieval-Augmented Generation
- Repository: GitHub - OpenBMB/SPARK
Method Overview
SPARK is a lightweight framework that learns adaptive knowledge participation policies for faithful retrieval-augmented generation. With the backbone LLM frozen, its participation policy dynamically regulates layer-wise FFN contributions, determining when and to what extent parametric knowledge participates during generation. Optimized through faithfulness-guided reinforcement learning using response-level rewards, SPARK mitigates catastrophic forgetting and better preserves general generation capabilities compared to full-parameter supervised fine-tuning.
Setup and Usage
Please refer to the official GitHub Repository for detailed environment setup, training, and evaluation scripts.
Citation
If you find this work useful, please cite the paper:
@misc{li2026sparkadaptiveparametricknowledge,
title={SPARK: Adaptive Parametric Knowledge Participation for Faithful Retrieval-Augmented Generation},
author={Ruochang Li and Pengcheng Huang and Zhenghao Liu and Yukun Yan and Huiyuan Xie and Yu Gu and Ge Yu and Maosong Sun},
year={2026},
eprint={2606.27786},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2606.27786},
}