The authors diagnose why LLMs collapse into repetitive, low-diversity outputs even when many valid continuations exist. They introduce a "validity-diversity" framework showing that the problem stems from two forms of miscalibration: (1) valid tokens aren't reliably ranked above invalid ones (order calibration), so sampling methods must trade validity for diversity, and (2) probability mass is overly concentrated on a few valid options with a long tail of mixed valid/invalid tokens (shape calibration). These local failures compound across decoding steps, sharply reducing sequence-level diversity. Experiments across 14 models confirm the pattern.
Main takeaways:
- Diversity collapse isn't just a sampling algorithm problem—it's baked into LLM probability distributions via miscalibration
- Order calibration failure: valid tokens don't consistently rank above invalid ones, forcing validity-diversity tradeoffs
- Shape calibration failure: probability mass is spiked on few valid options, with a heavy tail mixing valid and invalid
- Local miscalibration at each step compounds across decoding, producing large sequence-level diversity losses
- Observed across 14 models of different families and scales, suggesting a fundamental distribution problem