Post-Training & Forgetting

Terminology definition

LLM = foundation model ---(Post-Training = continual training = Alignment )----> Fine-tuned Model

• Foundation Model -> can be chat/instruct model or base/Pretrain model

Methods

1. Pre-train style
   • Example: Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages
   • Example: Examining Forgetting in Continual Pre-training of Aligned Large Language Models
2. SFT Style
   • Example: Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
   • Example: Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
   • Example: Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
3. RL style

Catastrophic Forgetting

• 這個是post training 最大的挑戰，他會忘記已有的技能．

Solutions of Catastrophic Forgetting

1. Experience Replay
   • Example:
     • GPT-2 --(task 1 training Data ) ----> GPT-2 ----(task 2 training Data + a little bit task 1 training Data )----> GPT-2
     • Paper: Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
2. Pseudo Experience Replay
   • if 找不到訓練資料 -> 讓 GPT-2 自己產生資料
   • 讓 LLaMa 自問自答 MMJ-Bench : A Comprehensive Study on Jailbreak Attacks and Defenses for Multimodal Large Language Models
3. Paraphrase
   • 用自己的話換句話說
   • Input: new
   • Output: new --> Foundation Model (換句話說) --> Old
4. Self-output
   • Input: new -> foundation Model -> output: old
   • Output: new
   • Paper: Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models

info

可能RL-based post training ( less forgetting? )

• Note: 也可以用別的model 去post training 不一定要foundation model
• Paper: I Learn Better If You Speak My Language