Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM
  Diversity
Post-training alignment often reduces LLM diversity, leading to a phenomenon
known as mode collapse. Unlike prior work that attributes this effect to
algorithmic limitations, we identify a fundamental, pervasive data-level
driver: typicality bias in preference data, whereby annotators systematically
favor familiar text as a result of well-established findings in cognitive
psychology. We formalize this bias theoretically, verify it on preference
datasets empirically, and show that it plays a central role in mode collapse.
Motivated by this analysis, we introduce Verbalized Sampling, a simple,
training-free prompting strategy to circumvent mode collapse. VS prompts the
model to verbalize a probability distribution over a set of responses (e.g.,
``Generate 5 jokes about coffee and their corresponding probabilities'').
Comprehensive experiments show that VS significantly improves performance
across creative writing (poems, stories, jokes), dialogue simulation,
open-ended QA, and synthetic data generation, without sacrificing factual
accuracy and safety. For instance, in creative writing, VS increases diversity
by 1.6-2.1x over direct prompting. We further observe an emergent trend that
more capable models benefit more from VS. In sum, our work provides a new
data-centric perspective on mode collapse and a practical inference-time remedy
that helps unlock pre-trained generative diversity.
68de624770ada21878c7500c
Jiayi Zhang
claimed_verified
2025-10-16T06:42:31.970Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/67e722482a5b717cadc91923/MEVtAX3evNon1i7_2J9y4.jpeg
Eleanor Zhang
user
EleanorZzz
68de624770ada21878c7500d
Simon Yu
claimed_verified
2025-10-15T15:27:10.468Z
636681feaa6a4af6073ba73e
https://cdn-avatars.huggingface.co/v1/production/uploads/636681feaa6a4af6073ba73e/u_0moYNu6as9Sszp-ej95.png
Simon Yu
user
simonycl
68de624770ada21878c7500e
Derek Chong
claimed_verified
2025-10-03T12:52:58.465Z
62813beb2a08e33da8684bec
/avatars/d2be5cf9bda833b56223caea7e79b4c0.svg
Derek
user
dcx
68de624770ada21878c7500f
Anthony Sicilia
68de624770ada21878c75010
Michael R. Tomz
68de624770ada21878c75011
Christopher D. Manning
68de624770ada21878c75012
Weiyan Shi
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM
  Diversity
Post-training alignment often reduces LLM diversity, leading to a phenomenon
known as mode collapse. Unlike prior work that attributes this effect to
algorithmic limitations, we identify a fundamental, pervasive data-level
driver: typicality bias in preference data, whereby annotators systematically
favor familiar text as a result of well-established findings in cognitive
psychology. We formalize this bias theoretically, verify it on preference
datasets empirically, and show that it plays a central role in mode collapse.
Motivated by this analysis, we introduce Verbalized Sampling, a simple,
training-free prompting strategy to circumvent mode collapse. VS prompts the
model to verbalize a probability distribution over a set of responses (e.g.,
``Generate 5 jokes about coffee and their corresponding probabilities'').
Comprehensive experiments show that VS significantly improves performance
across creative writing (poems, stories, jokes), dialogue simulation,
open-ended QA, and synthetic data generation, without sacrificing factual
accuracy and safety. For instance, in creative writing, VS increases diversity
by 1.6-2.1x over direct prompting. We further observe an emergent trend that
more capable models benefit more from VS. In sum, our work provides a new
data-centric perspective on mode collapse and a practical inference-time remedy
that helps unlock pre-trained generative diversity.
Typicality bias in preference data causes mode collapse in LLMs, and Verbalized Sampling is introduced as a prompting strategy to enhance diversity without compromising accuracy or safety.
LLM mode collapse typicality bias preference data cognitive psychology Verbalized Sampling creative writing dialogue simulation open-ended QA synthetic data generation factual accuracy safety