Chasing Away Repetitive LLM Responses with Verbalized Sampling

Chasing Away Repetitive LLM Responses with Verbalized Sampling

From Linear Digressions by Katie Malone

February 23, 2026 · 19 min

About this episode

This episode discusses verbalized sampling as a technique to reduce repetitiveness in LLM outputs for creative tasks.

One of the things that LLMs can be really helpful with is brainstorming or generating new creative content. They are called Generative AI, after all—not just for summarization and question-and-answer tasks. But if you use LLMs for creative generation, you may find that their output starts to seem repetitive after a little while. Let's say you're asking it to create a poem, some dialogue, or a joke. If you ask once, it'll give you something that sounds pretty reasonable. But if you ask the same thing 10 times, it might give you 10 things that sound kind of the same. Today's episode is about a technique called verbalized sampling, and it's a way to mitigate this repetitiveness—this lack of diversity in LLM responses for creative tasks. But one of the things I really love about it is that in understanding why this repetitiveness happens and why verbalized sampling actually works as a mitigation technique, you start to get some pretty interesting insights and a deeper understanding of what's going on with LLMs under the surface. The paper discussed in this episode is Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity https://arxiv.org/abs/2510.01171

People in this episode

Host: Katie Malone

Topics covered

  • Generative AI
  • LLM responses
  • creative content
  • verbalized sampling
  • mode collapse
  • diversity in AI

Keywords

  • LLM
  • Generative AI
  • creative generation
  • verbalized sampling
  • mode collapse
  • diversity
  • brainstorming

Mentioned in this episode

Organizations: arxiv

Books & works: Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity

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