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Learn how to automatically tag text with the usage of Large Language Models, and what are the trade-offs between different methods!
Paulo Maia on Aug 29, 2023
Text classification is one of the most common use cases in Natural Language Processing, with numerous practical applications – now easier to access with Large Language Models. Companies use text classification in multiple scenarios to become more efficient:
All of these use cases were solvable in the past without using LLMs. However, the uprising of these models has reduced the amount of necessary training data for obtaining good results, and has also increased the average performance of these use cases, taking less time for reaching them!
In this blog post, we will cover several techniques for text classification before the uprising of the most recent LLMs (OpenAI, LLaMA, Bing, …) and after.
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Send me the eBookThe most common techniques for text classification are:
“Let’s assume you’re an Encyclopedia, and you have to define the concepts I’m providing. Your explanation must be succinct (couple of paragraphs), like the summary section of a Wikipedia article talking about the concept. (…)”
Below is a comparative chart, summarizing the trade-offs of the methods in terms of required data, speed and accuracy.
We showed you several ways of doing text classification using Large Language Models. LLMs allow you to reach acceptable performance in a few hours of work and are pretty good for an initial benchmark – despite this, don’t forget about older methods, which can be a fallback when you want faster outcomes or when paying for LLMs’ requests is not feasible in the scale of your use case.
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