The difference lies in the number and length of text snippets analyzed.Īfter the QA extraction phase, result candidates are further classified with an ensemble of zero-shot classifiers on a wide variety of criteria (hate-speech, vulgar writing, spam, etc). The first one is QA (question answering): this model is used to try to extract a concrete answer, if any, from text snippets.īrave has been using LLMs for a while to improve search relevance, and this is an extension of what Brave Search already had in place to power its knowledge graph and featured snippets features. “The Summarizer is not powered by ChatGPT or its backend systems it is instead composed of three different LLMs 2 trained on different tasks: It uses existing technology that was developed for displaying knowledge graphs and featured snippets. ![]() ![]() The underlying technology of the Summarizer is a Large Language Model that was developed in-house.
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