|
--- |
|
license: mit |
|
tags: |
|
- code |
|
- genai |
|
- Aphasia |
|
- llm |
|
- nlp |
|
- master |
|
--- |
|
# AI-LA: Aphasia in Artificial Intelligence Large Language Models |
|
|
|
Most AI research focuses on adding capabilities to LLMs. In contrast, little has been done on how to remove these capabilities from pre-trained LLMs. |
|
|
|
Finding an approach that scores well on specificity and generalization |
|
|
|
A model editing technique scores well on specificity if related facts do not change after the model is edited. A technique scores well on generalization if the fact change is robust to adding or changing the context. |
|
|
|
There are three types of approaches to updating parameters - fine-tuning, hyper-networks, and causal tracking. |
|
|
|
In this model, I will test all three types! |
|
|
|
### MODEL GOAL |
|
Reproduce |
|
|
|
* Fine-tuning |
|
* Hyper-networks |
|
* causal tracking |
|
|
|
Talk to me: https://www.linkedin.com/in/alessandra-faria-b0816053/ |
