Key Points
1. Large Language Models (LLMs) have shown remarkable capabilities in natural language processing, storing vast amounts of factual and commonsense information.
2. Knowledge editing for LLMs aims to efficiently modify models' behaviors within specific domains while preserving overall performance across various inputs.
3. The paper proposes a unified categorization criterion that classifies knowledge editing methods into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge.
4. The paper introduces the new benchmark KnowEdit for comprehensive empirical evaluation of representative knowledge editing approaches across various tasks.
5. The study provides in-depth analysis and evaluation of knowledge editing methods on 12 NLP datasets, evaluating performance, usability, and underlying mechanisms.
6. Results show that SERAC, MEND, and FT-L demonstrate effectiveness in knowledge editing tasks, each showing strengths in edit success, portability, and locality.
7. The impact of knowledge editing on general tasks suggests that edited models maintained performance close to unedited models across diverse knowledge domains, with minor disruptions observed in some cases.
8. The findings highlight the effectiveness of knowledge editing methods in executing targeted factual updates with minimal disruptions to model performance in diverse knowledge domains.
9. The study sheds light on the potential applications and implications of knowledge editing in enhancing the utility and reliability of LLMs.
Summary
Comprehensive Review of Knowledge Editing for Large Language Models
In the study on Knowledge Editing for Large Language Models (LLMs), the paper provides a comprehensive review of the development and recent advances in knowledge editing for LLMs. The study aims to efficiently modify LLMs' behaviors within specific domains while preserving overall performance across various inputs. The paper categorizes knowledge editing methods into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge approaches. The paper introduces a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches and discusses the multifaceted applications of knowledge editing, including efficient machine learning, AI-generated content, trustworthy AI, and human-computer interaction.
The paper also evaluates the performance of different knowledge editing methods on various tasks and datasets and explores the impact of knowledge editing on general tasks. The study demonstrates the effectiveness of knowledge editing methods in executing factual updates with minimal disruptions to the model’s cognitive capabilities and adaptability across diverse knowledge domains. Additionally, the paper evaluates the performance of knowledge editing methods in a cross-domain setting, showcasing the robustness of certain methods in adapting to different datasets.
Taxonomy of Knowledge Editing Methods
The paper discusses the development and recent advances in knowledge editing for Large Language Models (LLMs), specifically focusing on the architecture of Transformers, knowledge storage in LLMs, and related techniques. It proposes a taxonomy to categorize knowledge editing methods based on educational and cognitive research theories and categorizes them into three approaches: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge. The paper presents extensive experiments on 12 NLP datasets to evaluate the performance, usability, and underlying mechanisms of knowledge editing methods.
It also explores the multifaceted applications of knowledge editing in efficient machine learning, AI-generated content, trustworthy AI, and human-computer interaction. The paper delves into specific methods such as LoRA and ROME, and their performance in continual editing, especially in mixed knowledge editing cases. It also discusses the limitations and advantages of different knowledge editing methods and presents a detailed error analysis of the deficiencies in current methods.
Furthermore, it compares various knowledge editing methods, emphasizing on methods that adjust the model's parameters and exploring the effectiveness of knowledge location techniques within LLMs. The research aims to bridge the gaps and offer insights into the mechanisms of knowledge in LLMs.
Reference: https://arxiv.org/abs/2401.01286