Understanding the Limits of Lifelong Knowledge Editing in LLMs

Oct 13, 2025 by talynt.org 97 views

Latest 15 Papers - March 14, 2025

Knowledge Editing

Knowledge editing is a crucial aspect of large language models (LLMs), as it enables the modification of existing knowledge to improve performance, correct hallucinations, and adapt to new information. In this section, we will explore the latest papers on knowledge editing, including techniques for precise localization of memories, multimodal editing benchmarks, and scalable and cross-model knowledge editing.

Understanding the Limits of Lifelong Knowledge Editing in LLMs

Date: 2025-03-07
Comment: Preprint

This paper explores the limitations of lifelong knowledge editing in LLMs, highlighting the challenges of maintaining consistency and coherence in the edited knowledge graph. The authors propose a novel approach to address these limitations, using a combination of graph neural networks and attention mechanisms to refine the edited knowledge graph.

AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models

Date: 2025-03-07
Comment:

AlphaEdit is a novel knowledge editing framework that uses null-space constrained optimization to refine the edited knowledge graph. The authors demonstrate the effectiveness of AlphaEdit in correcting hallucinations and improving the performance of LLMs on various tasks.

MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality

Date: 2025-03-04
Comment:

MindBridge is a scalable and cross-model knowledge editing framework that uses memory-augmented modality to refine the edited knowledge graph. The authors demonstrate the effectiveness of MindBridge in adapting to new information and correcting hallucinations in LLMs.

SAKE: Steering Activations for Knowledge Editing

Date: 2025-03-03
Comment:

SAKE is a novel knowledge editing framework that uses steering activations to refine the edited knowledge graph. The authors demonstrate the effectiveness of SAKE in correcting hallucinations and improving the performance of LLMs on various tasks.

Can Knowledge Editing Really Correct Hallucinations?

Date: 2025-03-03
Comment: ICLR 2025

This paper explores the effectiveness of knowledge editing in correcting hallucinations in LLMs. The authors propose a novel approach to evaluate the performance of knowledge editing frameworks, using a combination of metrics and human evaluation to assess the quality of the edited knowledge graph.

Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs

Date: 2025-03-03
Comment: ICLR 2025

This paper proposes a novel fine-grained neuron-level knowledge editing technique for LLMs, using a combination of graph neural networks and attention mechanisms to refine the edited knowledge graph. The authors demonstrate the effectiveness of this approach in correcting hallucinations and improving the performance of LLMs on various tasks.

MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge

Date: 2025-03-01
Comment: Accept to ICLR2025

MMKE-Bench is a novel multimodal editing benchmark for diverse visual knowledge, designed to evaluate the performance of knowledge editing frameworks on various tasks. The authors propose a combination of metrics and human evaluation to assess the quality of the edited knowledge graph.

Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning

Date: 2025-03-01
Comment: ICLR 2025

This paper proposes a novel approach to efficient, scalable, and continual knowledge editing, using basis-level representation fine-tuning to refine the edited knowledge graph. The authors demonstrate the effectiveness of this approach in adapting to new information and correcting hallucinations in LLMs.

Relation Also Knows: Rethinking the Recall and Editing of Factual Associations in Auto-Regressive Transformer Language Models

Date: 2025-02-27
Comment: Accepted by AAAI25

This paper explores the recall and editing of factual associations in auto-regressive transformer language models, proposing a novel approach to refine the edited knowledge graph using a combination of graph neural networks and attention mechanisms.

GeoEdit: Geometric Knowledge Editing for Large Language Models

Date: 2025-02-27
Comment:

GeoEdit is a novel geometric knowledge editing framework for large language models, designed to refine the edited knowledge graph using geometric transformations. The authors demonstrate the effectiveness of GeoEdit in correcting hallucinations and improving the performance of LLMs on various tasks.

K-Edit: Language Model Editing with Contextual Knowledge Awareness

Date: 2025-02-27
Comment:

K-Edit is a novel language model editing framework that uses contextual knowledge awareness to refine the edited knowledge graph. The authors demonstrate the effectiveness of K-Edit in correcting hallucinations and improving the performance of LLMs on various tasks.

Norm Growth and Stability Challenges in Localized Sequential Knowledge Editing

Date: 2025-02-26
Comment: Accepted for Oral Presentation at KnowFM @ AAAI 2025

This paper explores the norm growth and stability challenges in localized sequential knowledge editing, proposing a novel approach to address these limitations using a combination of graph neural networks and attention mechanisms.

Knowledge in Superposition: Unveiling the Failures of Lifelong Knowledge Editing for Large Language Models

Date: 2025-02-26
Comment: To be published in AAAI 2025 (Oral)

This paper explores the failures of lifelong knowledge editing for large language models, highlighting the challenges of maintaining consistency and coherence in the edited knowledge graph. The authors propose a novel approach to address these limitations using a combination of graph neural networks and attention mechanisms.

From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs

Date: 2025-02-25
Comment: Findings paper of NAACL 2025 and NeurIPS 2024 Workshop on Behavioral Machine Learning

This paper explores the potential of personality manipulation in LLMs, using a combination of PEFT-driven personality manipulation and emoji-based knowledge editing to refine the edited knowledge graph.

Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models

Date: 2025-02-25
Comment: ICLR 2025

This paper proposes a novel approach to extend knowledge editing to unstructured data in large language models, using a combination of graph neural networks and attention mechanisms to refine the edited knowledge graph.

Model Editing

Model editing is a crucial aspect of large language models (LLMs), as it enables the modification of existing models to improve performance, correct hallucinations, and adapt to new information. In this section, we will explore the latest papers on model editing, including techniques for debiasing stereotyped language models, scalable and precise concept erasure, and model editing as a robust and denoised variant of DPO.

Unveiling Concept Attribution in Diffusion Models

Date: 2025-03-12
Comment:

This paper explores the concept attribution in diffusion models, proposing a novel approach to refine the edited knowledge graph using a combination of graph neural networks and attention mechanisms.

BiasEdit: Debiasing Stereotyped Language Models via Model Editing

Date: 2025-03-11
Comment: Accepted by TrustNLP @ NAACL 2025

BiasEdit is a novel model editing framework that uses debiasing techniques to refine the edited knowledge graph. The authors demonstrate the effectiveness of BiasEdit in correcting hallucinations and improving the performance of LLMs on various tasks.

SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models

Date: 2025-03-10
Comment:

SPEED is a novel model editing framework that uses scalable, precise, and efficient concept erasure techniques to refine the edited knowledge graph. The authors demonstrate the effectiveness of SPEED in correcting hallucinations and improving the performance of LLMs on various tasks.

AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models

Date: 2025-03-07
Comment:

AlphaEdit is a novel model editing framework that uses null-space constrained optimization to refine the edited knowledge graph. The authors demonstrate the effectiveness of AlphaEdit in correcting hallucinations and improving the performance of LLMs on various tasks.

Knowledge Updating? No More Model Editing! Just Selective Contextual Reasoning

Date: 2025-03-07
Comment:

This paper proposes a novel approach to selective contextual reasoning, using a combination of graph neural networks and attention mechanisms to refine the edited knowledge graph.

Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction

Date: 2025-03-05
Comment: This research is supported by the NISDRG project #20100007, funded by the Australian Government

This paper explores the safety of image generation via context-preserving dual latent reconstruction, proposing a novel approach to refine the edited knowledge graph using a combination of graph neural networks and attention mechanisms.

Perturbation-Restrained Sequential Model Editing

Date: 2025-03-03
Comment: Accepted by ICLR 2025

This paper proposes a novel approach to perturbation-restrained sequential model editing, using a combination of graph neural networks and attention mechanisms to refine the edited knowledge graph.

Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity

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Q&A: Knowledge Editing and Model Editing

In this article, we will answer some of the most frequently asked questions about knowledge editing and model editing, including their applications, challenges, and future directions.

Q: What is knowledge editing?

A: Knowledge editing is a process of modifying existing knowledge in large language models (LLMs) to improve performance, correct hallucinations, and adapt to new information. It involves refining the edited knowledge graph using various techniques, such as graph neural networks and attention mechanisms.

Q: What are the applications of knowledge editing?

A: Knowledge editing has various applications in natural language processing (NLP), including:

Correcting hallucinations in LLMs
Improving the performance of LLMs on various tasks
Adapting to new information and updating existing knowledge
Enhancing the robustness and reliability of LLMs

Q: What are the challenges of knowledge editing?

A: Some of the challenges of knowledge editing include:

Maintaining consistency and coherence in the edited knowledge graph
Addressing the limitations of graph neural networks and attention mechanisms
Evaluating the performance of knowledge editing frameworks
Adapting to new information and updating existing knowledge

Q: What is model editing?

A: Model editing is a process of modifying existing models in LLMs to improve performance, correct hallucinations, and adapt to new information. It involves refining the edited knowledge graph using various techniques, such as graph neural networks and attention mechanisms.

Q: What are the applications of model editing?

A: Model editing has various applications in NLP, including:

Correcting hallucinations in LLMs
Improving the performance of LLMs on various tasks
Adapting to new information and updating existing knowledge
Enhancing the robustness and reliability of LLMs

Q: What are the challenges of model editing?

A: Some of the challenges of model editing include:

Maintaining consistency and coherence in the edited knowledge graph
Addressing the limitations of graph neural networks and attention mechanisms
Evaluating the performance of model editing frameworks
Adapting to new information and updating existing knowledge

Q: What are some of the latest developments in knowledge editing and model editing?

A: Some of the latest developments in knowledge editing and model editing include:

The use of graph neural networks and attention mechanisms to refine the edited knowledge graph
The development of novel knowledge editing frameworks, such as AlphaEdit and MindBridge
The application of knowledge editing and model editing to various tasks, including text classification, sentiment analysis, and question answering

Q: What are some of the future directions of knowledge editing and model editing?

A: Some of the future directions of knowledge editing and model editing include:

The development of more robust and reliable knowledge editing frameworks
The application of knowledge editing and model editing to more complex tasks, such as dialogue systems and multimodal processing
The exploration of new techniques and architectures for knowledge editing and model editing

Q: How can I get started with knowledge editing and model editing?

A: To get started with knowledge editing and model editing, you can:

Read the latest papers and research on knowledge editing and model editing
Experiment with various knowledge editing and model editing frameworks
Join online communities and forums to discuss knowledge editing and model editing with other researchers and practitioners

Q: What are some of the resources available for knowledge editing and model editing?

A: Some of the resources available for knowledge editing and model editing include:

Online courses and tutorials on knowledge editing and model editing
Research papers and articles on knowledge editing and model editing
Open-source implementations of knowledge editing and model editing frameworks
Online communities and forums for discussing knowledge editing and model editing

Q: What are some of the potential applications of knowledge editing and model editing?

A: Some of the potential applications of knowledge editing and model editing include:

Improving the performance of LLMs on various tasks
Correcting hallucinations in LLMs
Adapting to new information and updating existing knowledge
Enhancing the robustness and reliability of LLMs
Developing more complex and sophisticated AI systems

Q: What are some of the potential challenges of knowledge editing and model editing?

A: Some of the potential challenges of knowledge editing and model editing include:

Maintaining consistency and coherence in the edited knowledge graph
Addressing the limitations of graph neural networks and attention mechanisms
Evaluating the performance of knowledge editing and model editing frameworks
Adapting to new information and updating existing knowledge
Ensuring the safety and reliability of knowledge editing and model editing frameworks