AI model editing techniques

 chatgpt output, un-edited. Some wrong links and erorrs are apparent. 

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1. Fine-Tuning

• Definition: Adjusting pre-trained models by retraining them on a specific dataset to tailor them to a particular task or domain.
• References:
  • Fine-Tuning Language Models
  • Full and Parameter-Efficient Fine-Tuning

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2. Prompt Engineering

• Definition: Crafting specific inputs (prompts) to guide the behavior of large language models without altering their parameters.
• References:
  • Prompting for NLP
  • Chain-of-Thought Prompting

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3. Model Editing via Retrieval-Augmented Generation (RAG)

• Definition: Integrating external databases or retrieval systems to improve or adapt the model's outputs without direct parameter changes.
• References:
  • RAG Explained
  • Dynamic Knowledge Updates

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4. Knowledge Injection

• Definition: Incorporating domain-specific knowledge into a model post-training.
• References:
  • Knowledge-Integrated Language Models
  • Knowledge Graph Integration

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5. Soft Prompt Tuning

• Definition: Learning a set of prompt tokens that adjust the behavior of pre-trained models without altering core weights.
• References:
  • Soft Prompt Tuning Explained
  • Prompt-Based Learning

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6. Modular Transfer Learning

• Definition: Dividing models into modules (e.g., embeddings, encoders, decoders) and only updating or replacing specific components.
• References:
  • Modular Learning for NLP
  • Parameter-Efficient Transfer Learning

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7. Dynamic Reweighting

• Definition: Adjusting the influence of certain parts of the model during inference based on specific tasks or inputs.
• References:
  • Sparsity-Based Optimization
  • Attention Mechanisms

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8. Model Surgery

• Definition: Directly modifying neural network weights, layers, or architectures post-training.
• References:
  • MEMIT: Model Surgery Techniques
  • Layerwise Editing

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9. Continual Learning

• Definition: Allowing a model to learn new information over time without forgetting prior knowledge.
• References:
  • Elastic Weight Consolidation
  • Continual Learning Overview

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10. Gradient Editing

• Definition: Directly modifying gradients during training to induce specific behaviors or rectify known issues.
• References:
  • Gradient Editing Explained
  • Debugging Neural Models

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11. Reinforcement Learning from Human Feedback (RLHF)

• Definition: Using human evaluations to fine-tune models, particularly for aligning AI with desired ethical or stylistic outcomes.
• References:
  • RLHF Overview
  • OpenAI's Use of RLHF

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12. Model Patching

• Definition: Adding or replacing specific components in a model with updated or improved modules.
• References:
  • Knowledge Patch
  • Error Correction Patching

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13. Parameter-Free Updating

• Definition: Techniques like black-box optimization or external decision systems that modify behavior without changing core parameters.
• References:
  • Black-Box Optimization
  • Decision-Based Editing

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14. Memory Editing

• Definition: Directly modifying or updating specific "memories" in a model, allowing it to adjust responses to certain inputs or queries without retraining.
• Techniques:
  • MEMIT (Model Editing Made Informed by Targeting)
  • ROME (Rank-One Model Editing)
• References:
  • MEMIT
  • ROME
  • Direct Model Memory Editing

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15. Multi-Modal Model Editing

• Definition: Modifying models trained on multi-modal data (e.g., text and images) for domain-specific applications.
• References:
  • Multi-Modal Learning
  • Vision-Language Consistency

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16. Federated Learning Adjustments

• Definition: Decentralized learning where updates are based on data from multiple users without directly sharing datasets.
• References:
  • Federated Learning Overview
  • Privacy-Preserving Federated Learning

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17. Meta-Learning (Learning to Learn)

• Definition: Training models to quickly adapt to new tasks with minimal data by leveraging meta-learning algorithms.
• References:
  • MAML: Model-Agnostic Meta-Learning
  • Meta-Learning Overview

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This version now includes Memory Editing with references to emerging techniques like MEMIT and ROME for direct manipulation of model-specific knowledge.