The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Localizing and Editing Knowledge in LLMs with Peter Hase - #679
Apr 8, 2024
Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab, dives into the fascinating world of large language models. He discusses the vital role of interpretability in AI, exploring how knowledge is stored and accessed. The conversation shifts to model editing, emphasizing the challenges of deleting sensitive information while maintaining data integrity. Hase also highlights the risks of easy-to-hard generalization in releasing open-source models and the impact of instructional prompts on model performance. This insightful dialogue unravels complexities in AI decision-making.
49:46
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Quick takeaways
- Interpretability is crucial for understanding language models' reasoning processes and building trust in model responses.
- Model editing challenges traditional beliefs by revealing unintuitive methods for pinpointing knowledge storage within language models.
Deep dives
Interpretability in Language Models
The podcast episode delves into Peter Hossie's research areas during his PhD, highlighting three key focuses. Firstly, interpretability deals with understanding language models' internal reasoning processes, emphasizing the importance of trust in these models' responses. Secondly, model editing aims to update factual knowledge within language models, revealing challenges in pinpointing where information is stored within the model. Lastly, scalable oversight addresses supervising AI systems as they improve at tasks, where understanding model interpretability can enhance overall safety measures.
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