Lilian Weng's blog post discusses controllable neural text generation, a technique for generating text that meets specific requirements. The post covers various methods, including prompt design, which involves crafting input prompts to influence the generated text. P-tuning and Prompt Tuning are also discussed as ways to control the output. Unlikelihood training is another method mentioned, where the model learns to avoid certain words or phrases. These techniques aim to improve the coherence and relevance of generated text. --- Why it matters: This matters because controllable neural text generation has applications in areas like language translation, chatbots, and content creation, where generated text needs to meet specific requirements such as tone, style, or topic. Source: https://lilianweng.github.io/posts/2021-01-02-controllable-text-generation/