This might sound like a weird approach at first, but hear me out:

Traditionally, when we think about training a language model (like GPT-3 or BERT), we feed it a bunch of text and ask it to predict the next word in that sequence. But what if instead of generating new words, we asked our model to identify which words are missing from an existing sentence? This is where Electra comes in:

Instead of training our model to generate new sentences (like GPT-3), we train it to identify which words are missing from a given input. For example, let’s say we have the following sentence: “The quick brown fox jumps over _______.” Instead of generating a word to fill in that blank space, our Electra model would be trained to predict what word is most likely to complete that thought based on context and language patterns.

So how does it work? Well, instead of using the traditional “next-word prediction” approach, we train our model to identify which words are missing from a given input by feeding it two inputs: one with all the words present (the “complete sentence”), and another with some of the words removed (“incomplete sentence”). The model then has to predict whether or not each word is present in the complete sentence based on context.

For example, let’s say we have the following input: “The quick brown fox jumps over _______.” Our Electra model would be trained to identify which words are most likely to fill in that blank space by comparing it against a database of millions of other sentences (like GPT-3). If our model predicts that the word “dog” is most likely to complete that thought based on context and language patterns, then we can use that prediction to generate new text or improve existing content.

Electra: a fancy way of saying “let’s train our language model to identify what words are missing from a sentence instead of generating new ones”. It might sound weird at first, but trust me this approach has some serious benefits when it comes to improving the accuracy and relevance of your content.