Localizing and Editing Knowledge in LLMs with Peter Hase - #679
Published:Apr 8, 2024 21:03
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode featuring Peter Hase, a PhD student researching NLP. The discussion centers on understanding how large language models (LLMs) make decisions, focusing on interpretability and knowledge storage. Key topics include 'scalable oversight,' probing matrices for insights, the debate on LLM knowledge storage, and the crucial aspect of removing sensitive information from model weights. The episode also touches upon the potential risks associated with open-source foundation models, particularly concerning 'easy-to-hard generalization'. The episode appears to be aimed at researchers and practitioners interested in the inner workings and ethical considerations of LLMs.
Key Takeaways
- •The episode explores methods for understanding how LLMs make decisions, focusing on interpretability.
- •It discusses the debate on how LLMs store knowledge and the importance of removing sensitive information.
- •The article highlights the potential risks associated with open-source foundation models and 'easy-to-hard generalization'.
Reference
“We discuss 'scalable oversight', and the importance of developing a deeper understanding of how large neural networks make decisions.”