Distribution Prompting: Understanding the Expressivity of Language Models Through the Next-Token Distributions They Can Produce

• URL: http://arxiv.org/abs/2505.12244v1
• Date: Sun, 18 May 2025 05:49:48 GMT
• Title: Distribution Prompting: Understanding the Expressivity of Language Models Through the Next-Token Distributions They Can Produce
• Authors: Haojin Wang, Zining Zhu, Freda Shi,
• Abstract summary: We show that some distributions are significantly harder to elicit than others.<n>We find that distributions with very low or very high entropy are easier to approximate than those with moderate entropy.
• Score: 10.369289331969098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive neural language models (LMs) generate a probability distribution over tokens at each time step given a prompt. In this work, we attempt to systematically understand the probability distributions that LMs can produce, showing that some distributions are significantly harder to elicit than others. Specifically, for any target next-token distribution over the vocabulary, we attempt to find a prompt that induces the LM to output a distribution as close as possible to the target, using either soft or hard gradient-based prompt tuning. We find that (1) in general, distributions with very low or very high entropy are easier to approximate than those with moderate entropy; (2) among distributions with the same entropy, those containing ''outlier tokens'' are easier to approximate; (3) target distributions generated by LMs -- even LMs with different tokenizers -- are easier to approximate than randomly chosen targets. These results offer insights into the expressiveness of LMs and the challenges of using them as probability distribution proposers.

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