[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.

Must-read Papers on Knowledge Editing for Large Language Models.

EMNLP'23 survey: a curation of awesome papers and resources on refreshing large language models (LLMs) without expensive retraining.

[NeurIPS 2024] Knowledge Circuits in Pretrained Transformers

Official code repo for "Editing Implicit Assumptions in Text-to-Image Diffusion Models"

[EMNLP 2024] To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models

[ACL 2024] Learning to Edit: Aligning LLMs with Knowledge Editing

[ACL 2024] An Easy-to-use Hallucination Detection Framework for LLMs.

[ICLR 2024] Unveiling the Pitfalls of Knowledge Editing for Large Language Models

OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System.

Code and dataset for the paper: "Can Editing LLMs Inject Harm?"

Knowledge Unlearning for Large Language Models

Official codes for COLING 2024 paper "Robust and Scalable Model Editing for Large Language Models": https://arxiv.org/abs/2403.17431v1

Can Knowledge Editing Really Correct Hallucinations? (ICLR 2025)

Stable Knowledge Editing in Large Language Models

Official implementation for Zhong & Le et al., GNNs Also Deserve Editing, and They Need It More Than Once. ICML 2024

Debiasing Stereotyped Language Models via Model Editing

How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training

COLING 2025: MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models

Circuit-Aware Editing Enables Generalizable Knowledge Learners