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Quantizing Transformers by Helping Attention Heads Do Nothing with Markus Nagel - #663

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

CHAPTER

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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