---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---

# SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation

This repository contains the model checkpoints for **SHIFT**, a lightweight framework designed to resolve knowledge conflicts in retrieval-augmented generation (RAG).

- **Paper:** [SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation](https://huggingface.co/papers/2606.27786)
- **Repository:** [GitHub - OpenBMB/SHIFT](https://github.com/OpenBMB/SHIFT)

## Method Overview

SHIFT reformulates neuron-level modification as a learnable gate modulation, allowing LLMs to adaptively regulate internal activations for knowledge conflict resolution. Technically, SHIFT equips LLMs with a lightweight gate module and optimizes fewer than 0.01% trainable parameters while keeping the backbone model frozen. During generation, the gate module adjusts the model's internal representations to adaptively leverage contextual and parametric knowledge.

## Setup and Usage

Please refer to the official [GitHub Repository](https://github.com/OpenBMB/SHIFT) for detailed environment setup, training, and evaluation scripts.

## Citation

If you find this work useful, please cite the paper:

```bibtex
@misc{li2026shiftgatemodulatedactivationsteering,
      title={SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in 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}, 
}
```