Verbalized Sampling: Overcoming LLM Mode Collapse for Enhanced Diversity

This paper introduces Verbalized Sampling (VS), a novel prompting strategy designed to mitigate mode collapse in Large Language Models (LLMs). Mode collapse, a phenomenon where LLMs generate repetitive and predictable outputs, limits their creative potential and overall usefulness. The authors identify typicality bias in preference data as a key driver of mode collapse. This bias arises because annotators tend to favor familiar text, leading to a skewed training signal that reinforces common patterns. Unlike previous approaches that focus on algorithmic limitations, this work highlights the importance of data-level factors in shaping LLM behavior.

Verbalized Sampling addresses mode collapse by prompting the model to verbalize a probability distribution over a set of responses. This encourages the model to explore a wider range of possibilities and generate more diverse outputs. The authors demonstrate the effectiveness of VS across a variety of tasks, including creative writing, dialogue simulation, open-ended QA, and synthetic data generation. The results show that VS significantly improves diversity without sacrificing factual accuracy or safety. Furthermore, the authors observe that more capable models benefit more from VS, suggesting that the technique can unlock even greater potential as models continue to improve. This research provides a valuable data-centric perspective on mode collapse and offers a practical inference-time remedy that can be easily implemented without retraining the model. The findings have significant implications for the development and deployment of LLMs, paving the way for more creative, versatile, and engaging AI applications.

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