Fine-Tuning LLMs Is a Waste of Time
a year ago
- #AI
- #LLMs
- #Fine-Tuning
- Fine-tuning LLMs for knowledge injection is ineffective and can overwrite existing knowledge.
- Neurons in trained LLMs are densely interconnected; updating them risks losing valuable information.
- Modular methods like retrieval-augmented generation (RAG), adapters, and prompt-engineering are safer alternatives.
- Fine-tuning advanced LLMs can lead to unexpected and problematic downstream effects.
- Techniques such as RAG and LoRA allow for knowledge insertion without altering the core model.
- The article emphasizes the importance of preserving the integrity of a model's foundational knowledge.