Implications of Predicting the Next Token
The article discusses the common misconception surrounding the concept of predicting the next token in language models. It contrasts the capabilities of Markov chains with those of more advanced language models, emphasizing that the latter can produce more nuanced and coherent text. The author illustrates the limitations of Markov chains and highlights the importance of understanding the sophistication of modern language generation techniques.
- ▪Many people struggle to grasp what it means to predict the next token in language models.
- ▪Markov chains are incapable of producing meaningful text, often resulting in gibberish.
- ▪Advanced language models achieve a level of sophistication that far surpasses the outputs of Markov chains.
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Opening excerpt (first ~120 words) tap to expand
I find that a lot of people have trouble with this concept of predicting the next token. And by trouble, I mean that they struggle to understand what it actually means to predict the next token. It seems simpler than it is. Because when you say "predict the next token," I think what most people think of is the Markov chain intuition that you have a big table of statistics, and then you look at what word is the next most likely, and then you pick that as the word. The thing about this is that if you have ever used a Markov chain, you would know that Markov chain text is complete gibberish. Markov chain text does not resemble meaningful writing.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Lesswrong.
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