zjunlp/EasyEdit

Features

• General Knowledge Modification - Inserts new information, updates outdated facts, or erases sensitive data from internal model parameters.
• Parameter-Based Knowledge Editing - Provides a toolkit for updating specific facts by modifying internal model parameters.
• Activation Steering Vectors - Adjusts model personality and reasoning in real time using steering-vector activation shifts.
• Conceptual Knowledge Editing - Enables updating or erasing specific conceptual information within large language models using targeted editing methods.
• External Memory Knowledge Insertion - Implements memory-based insertion to update model knowledge without modifying internal weights.
• Knowledge Edit Impact Analysis - Measures the success of edits by analyzing reliability, generalization, and portability.
• Knowledge Edit Locality Evaluators - Provides specialized metrics to ensure knowledge edits are local and do not corrupt unrelated information.
• Knowledge Editing Frameworks - Functions as a comprehensive framework for targeted knowledge updates and erasure in large language models.
• Knowledge Update Management - Modifies specific facts, behaviors, or unwanted information within a language model using various editing methods.
• LLM Benchmarking - Measures the success of knowledge edits using reliability, generalization, and locality metrics across datasets.
• LLM Knowledge Editing - Provides the primary capability to update or remove specific facts and conceptual information within LLMs.
• Meta-Network Weight Predictors - Uses meta-networks to predict necessary weight updates for precise factual modifications.
• Model Behavior Correction - Adjusts model responses to eliminate toxic behaviors or change personality traits and opinions.
• Model Steering Tools - Provides tools to adjust model personality and reasoning patterns using steering vectors.
• Multimodal Architecture Editing - Modifies specific facts or behaviors in large multimodal models across diverse model architectures.
• Multimodal Knowledge Editing - Allows modifying or removing specific information within models that handle multiple data types to ensure consistency.
• Model Modification - Modifies knowledge and behaviors in models that process both images and text for tasks like image captioning.
• Editing Accuracy Metrics - Implements specific accuracy metrics to evaluate the reliability and locality of knowledge edits.
• Reasoning Steering - Adjusts model outputs in real time using steering vectors to modify personality or reasoning patterns.
• Toxic Behavior Removal - Locates and edits specific toxic regions within a model using targeted instances to neutralize harmful outputs.
• AI Evaluation Suites - Provides a comprehensive suite of metrics to evaluate the reliability and generalization of model edits.
• Batch Knowledge Editing - Provides capabilities to update multiple pieces of knowledge across a model in groups to improve processing efficiency.
• In-Context Learning Editing - Updates model responses using retrieved sets of demonstrations to guide the model toward the correct answer.
• Language-Specific Knowledge Editing - Enables updating, inserting, or erasing linguistic facts and logic traps, including support for Chinese language data.
• LLM Parameter Modifiers - Enables targeted weight updates and sequential knowledge edits across various model architectures.
• Model Personality Editing - Modifies personality traits by editing opinions on specific topics based on psychological frameworks.
• Sequential Update Strategies - Allows models to accumulate multiple knowledge updates through a sequence of parameter modifications.
• Multimodal Knowledge Editors - Allows the modification of knowledge and behaviors in models processing both image and text modalities.
• Prompt-Based Knowledge Editing - Updates model responses by providing new facts directly in the prompt without modifying model parameters.
• Safety and Alignment Frameworks - Locates and edits specific model regions to remove toxic behaviors and neutralize harmful outputs.
• Sequential Knowledge Edits - Applies a series of knowledge updates continuously to model weights without resetting after every change.
• Steering Method Composition - Combines multiple steering techniques and tunes intervention strength to achieve fine-grained control over model activations.
• Editing Network Pretraining - Trains meta-networks or classifiers required by specific editing methods to prepare the model for knowledge updates.
• Knowledge Editor Training - Provides tools to train a model editor using specific hyperparameters and datasets for targeted knowledge updates.
• Multimodal Embedding Generation - Creates embedding files required by specific editing methods to facilitate the modification of multimodal knowledge.
• Multimodal Embeddings - Generates and modifies shared embedding spaces to maintain consistency between text and image knowledge.
• Evaluation Benchmarks - Detoxifies models using knowledge editing techniques.
• Model Development Tools - Tooling for modifying and editing large language models.
• Model Utilities - Framework for editing knowledge within language models.
• Jailbreak Defenses - Detoxifies models through targeted knowledge editing.

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