Diversity collapse in LLMs arises from order and shape miscalibration in token probability distributions at inference time, not from sampling methods.
Mind the gap: Con- formative decoding to improve output diversity of instruction-tuned large language models
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Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs
Diversity collapse in LLMs arises from order and shape miscalibration in token probability distributions at inference time, not from sampling methods.