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LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, [3] with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London.
As originally proposed by Google, [16] each CoT prompt included a few Q&A examples. This made it a few-shot prompting technique. However, according to a researchers at Google and the University of Tokyo, simply appending the words "Let's think step-by-step", [ 25 ] has also proven effective, which makes CoT a zero-shot prompting technique.
Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning -based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.
Police have arrested a woman who they say kidnapped her own children from a Virginia bus stop on Thursday, triggering an Amber Alert. The Augusta County Sheriff's Office said late Thursday that ...
At least six Democrats in Congress say they were targeted with a bomb threat over the Thanksgiving holiday. Four Democratic representatives from Connecticut said Thursday they were targeted with ...
A food safety expert weighs in on flour bugs, also known as weevils, that can infest your pantry after one TikToker found her flour infested with the crawlers.
The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. [1]