![]() ![]() Now imagine getting a machine to do this. You've got to juggle meaning, emotion, rhythm, and on top of all that, there's the issue of syllables. As we'll discuss, prompting and training are probably not the shortest path to developing a sophisticated poetry generator.Ĭrafting a good piece of poetry is tough work, even for humans. But they're not the only way to modify the behavior of language models and, in some cases, they're far from the best solution.įor example, poetry and lyrics generation remains quite difficult for even state of the art language models like GPT-4. After all, they're the most common methods to get these models to do what we want them to do. When we want to improve a large language model's behavior, prompt engineering and training are often the first solutions we reach for. Write a poem that explores the concept of time and its impact on our lives-nostalgia, the passage of time, the fleeting nature of moments: ![]() You can use it to rewrite Twinkle, Twinkle Little Star or generate modernist poems with lots of beautiful white space sprinkled with lines of lengths that you choose.įor example, we asked Poet Vicuna-13B to write an eight line poem with these syllable counts in response to this prompt: Poet Vicuna-13B is an implementation of Vicuna-13B that is modified to generate poems and lyrics with specific syllabic patterns. This model is part of an early-stage project focused on enhancing open source large language models. In this post, we discuss a version of Vicuna-13B that we just released called Poet Vicuna-13B. ![]()
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