带入您自己的知识：大型语言模型（LLM）知识扩展方法综述_17页_417kb

Summary of "Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion"

Core Content

This paper presents a comprehensive survey of methods for expanding the knowledge of large language models (LLMs), focusing on adapting them to new factual, domain-specific, language, and user preference knowledge. It categorizes the methods into three main types: continual learning, model editing, and retrieval-based approaches, and evaluates their applicability and limitations across different knowledge types and criteria.

Main Views

• LLMs need to be updated to remain effective and relevant in real-world applications due to their static knowledge cutoff.
• There are three primary strategies for knowledge expansion: continual learning, model editing, and retrieval-based methods.
• Continual learning involves incremental updates to the model's parameters, allowing it to adapt to new information without full retraining.
• Model editing enables precise, controllable modifications to the model's knowledge, typically at the parameter level, for specific inputs or tasks.
• Retrieval-based methods dynamically access external knowledge sources during inference, avoiding parameter updates and enabling real-time access to up-to-date information.

Key Information

1. Knowledge Types

The paper defines four key types of knowledge that LLMs can be adapted to:

• Factual Knowledge: General truths and real-time information.
• Domain Knowledge: Specialized information relevant to specific fields like medicine, law, or engineering.
• Language Knowledge: Ability to understand, generate, and reason in specific natural or programming languages.
• Preference Knowledge: Alignment with user-specific needs, values, and expectations.

2. Continual Learning

• Definition: A machine learning paradigm that allows models to learn continuously without forgetting previous knowledge.
• Applications:
  • Factual Updates: Methods like Glot500, EMMA-500, and Aya use continual pretraining to expand language coverage.
  • Domain Adaptation: Techniques such as DEMix-DAPT, ELLE, and Lifelong-MoE focus on adapting LLMs to new domains using domain-specific data.
  • Language Expansion: CPT enables multilingual and programming language adaptation, improving performance in underrepresented languages.
  • Preference Alignment: CPA (Continual Preference Alignment) ensures models adapt to evolving user preferences while preserving ethical and safe behavior.
• Limitations:
  • Lack of precise control compared to model editing and retrieval.
  • High computational cost for large-scale retraining.
  • Limited applicability in black-box models.

3. Model Editing

• Definition: A method that allows for targeted and efficient updates to specific parts of the model.
• Applications:
  • Factual Updates: Techniques like KnowledgeEditor, MEND, ROME, and MEMIT enable precise edits to factual knowledge.
  • Preference Updates: Model editing is used to detoxify LLMs, reduce bias, and protect privacy by modifying specific model parameters.
• Limitations:
  • Limited to structured, relational, and instance-level knowledge.
  • May affect general model capabilities and downstream performance.
  • Challenges with locality and gradual forgetting in large-scale edits.

4. Retrieval-based Methods

• Definition: Explicitly access external knowledge during inference without modifying model parameters.
• Applications:
  • Factual Updates: Retrieval-augmented generation (RAG) and retrieval-based in-context learning enable dynamic access to current factual information.
  • Domain Adaptation: Retrieval-based methods are effective in specialized domains like biomedicine and finance by integrating external knowledge.
• Limitations:
  • Reliance on external sources can lead to inconsistencies or outdated information.
  • Performance depends heavily on the quality and scope of the retrieval system.
  • Lack of knowledge consistency across queries due to static model parameters.

Comparison Table

Challenges and Opportunities

• Challenges:
  • Knowledge conflicts, including temporal misalignment, model inconsistency, and hallucinations.
  • Balancing knowledge retention with new updates.
  • Ensuring consistency and reliability across different knowledge types and tasks.
• Opportunities:
  • Enhancing LLMs' adaptability and robustness.
  • Improving performance in underrepresented languages and specialized domains.
  • Developing more efficient and controllable methods for knowledge expansion.
• Guidelines:
  • Use continual learning for large-scale and long-term knowledge updates.
  • Apply model editing for precise and localized knowledge modifications.
  • Leverage retrieval-based methods for dynamic access to external knowledge.
  • Combine these approaches to achieve comprehensive and reliable knowledge expansion in LLMs.