
Quantizing Transformers by Helping Attention Heads Do Nothing with Markus Nagel - #663
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Enhancing LLMs through Self-Verification
This chapter explores the scalability of transformers and their relevance to large language models (LLMs), focusing on the challenges they face in complex reasoning tasks. It introduces the concept of 'chain of thought prompting' and suggests a new self-verification process to improve the accuracy and reliability of LLM outputs.
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