Self-Consistency Improves Chain of Thought Reasoning in Language Models | 5 Minute Paper Podcast

Описание: 📄 Self-Consistency Improves Chain of Thought Reasoning in Language Models

🔗 Paper: https://doi.org/10.1007/978-3-642-337....
👥 Authors: Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi (+3 more)
📅 Published: 2022 | arXiv:cs.CL
🏷️ Topics: language, models, reasoning, prompting, complex

ABSTRACT:
Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuitio...

TIMESTAMPS:
00:00 - Introduction
01:06 - Wait, so Chain-of-Thought already made...
02:25 - Greedy decoding? So, its like...
03:34 - Hmm, so its like asking...
04:40 - Okay, so its simple to...
05:49 - You mentioned sample-and-rank earlier. How...
06:56 - This consistency idea seems to...
08:01 - Thats incredibly exciting! Any downsides...
09:12 - My pleasure, Chuck! Always great...

DISCLAIMER:
This video contains AI-generated synthetic voices inspired by public figures. These voices are artificially created and do not represent the real persons. This content is for educational and research purposes only and is not affiliated with, endorsed by, or sponsored by Chuck Nice, Neil deGrasse Tyson, or any associated organizations.

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