Add model card, update pipeline tag and library name

README.md

@@ -1,6 +1,38 @@
 ---
-license: apache-2.0
 language:
 - en
-pipeline_tag: question-answering
----
+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}, 
+}
+```