Advanced Reasoning with Large Language Models with Chain of Thought Prompting | Paper explained!

Abstract: We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking. For instance, prompting a 540B-parameter language model with just eight chain of thought exemplars achieves state of the art accuracy on the GSM8K benchmark of math word problems, surpassing even finetuned GPT-3 with a verifier.

#artificialintelligence #nlproc #nlp #deeplearning #ml

[andybaker]

She's a robot, right? She's good. It was her occasionally unnatural inflection that tipped me off.

[ChaoticNeutralMatt]

That uncanny valley consistency. So weird.

[temanapotaka-dewes]

CoT prompting seems to be a logical solution to getting the LLM to do what you want it to do.



What are the limitations of CoT prompting?

[jasonwong]

The problem with cot is that it requires the human user to know how to solve that sort of problem and if they knew that, it'd be faster for them to solve the problem themselves. Also it just requires more work from the user.

[hailking]

Great videos! I have question regarding the data, did they actually added the chain of thoughts to all of the training data, or only some of them?

[WillGilpin]

Youtube will add subtitles if we need them. No need to add them to the videos - they are distracting

[best_songs_ever]

is the host also computer generated using AI...!!🤔

[lemark]

nothing new, She didnt even bother to run a test herself lol waste of time.