Papers with Code 아카이브
Model Editing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Detoxifying Large Language Models via Knowledge Editing
BadEdit: Backdooring large language models by model editing
Editing Massive Concepts in Text-to-Image Diffusion Models
VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Efficiently Quantifying and Mitigating Ripple Effects in Model Editing
Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing
Editing Conceptual Knowledge for Large Language Models
Consecutive Batch Model Editing with HooK Layers
AKEW: Assessing Knowledge Editing in the Wild
"Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond
Potential and Challenges of Model Editing for Social Debiasing
Knowledge Graph Enhanced Large Language Model Editing
Stable Knowledge Editing in Large Language Models
Event-level Knowledge Editing
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
Learning to Edit: Aligning LLMs with Knowledge Editing
Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)
Towards Uncovering How Large Language Model Works: An Explainability Perspective
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing
Dense Passage Retrieval: Is it Retrieving?
Model Editing by Standard Fine-Tuning
The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse
Long-form evaluation of model editing
Knowledge Editing on Black-box Large Language Models
Rethinking Machine Unlearning for Large Language Models
Model Editing with Canonical Examples
On the Robustness of Editing Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.