Structured Output Collapses Answer Diversity Across 44 Language Models

2026-07-20

AI Summary

This study investigates how structured output formats, such as JSON, implicitly constrain the response diversity of large language models within open-ended answer spaces. By imposing format instructions like JSON on 44 models using the One-Word Census benchmark and employing control experiments, resampling, information entropy analysis, and statistical significance testing, the work provides the first systematic quantification of the distributional compression effect induced by output formatting. Results reveal that JSON requests increase the modal answer frequency from 41% to 64%, reduce the number of unique responses, and lower average surprisal. This effect stems not from decoding constraints but from models’ reliance on structured linguistic registers encountered during training, and it is closely associated with tool-use-oriented post-training.

Abstract

When a language model must choose one answer from a large space of equally valid options, a format clause -- "Reply with JSON only" -- changes which answer it chooses. We re-run the One-Word Census (arXiv:2607.12796): 31 wide-answer-space category prompts asked of 44 models, now with the reply requested in JSON -- no schema enforcement, no constrained decoding, only the request. Convergence deepens sharply: on the unconstrained "Pick a word" prompt the modal answer rises from 41% to 64% of the pool and distinct answers fall from 52 to 36; mean answer-choice surprisal drops from 1.80 to 1.58 bits. The tax is progressive: six of 44 models move individually (BH-FDR q=.10), all toward the mode, led by the most distinctive models, while the conformist floor is immobile. It is a sharpener, not a re-indexer -- the plain-chat modal answer survives in 28 of 31 categories. Defaults are register-indexed: a within-run re-sample (n=20) finds JSON shifts 53% of a model's stable chat defaults, mostly back to the crowd, and installs defaults absent from chat (Claude Fable 5 answers "cerulean" for colour 0% of the time in chat, 100% in JSON). Full-battery controls reveal a register gradient: compression is significant and specific to the answer-delivery formats models are trained to speak (JSON -0.22 bits, p=.0002; XML -0.19, p=.002), absent for YAML and CSV, and reversed for an arbitrary bracket wrapper (+0.13, p=.009) -- weighing the mechanism toward tool-use post-training. Enforcing the schema at the decoder (response_format) compresses no further than the request (-0.03 bits): the collapse lives in the model's response to the register, not the decoder. Structured output is how software consumes language models, and that surface is served by a measurably more homogeneous model than the chat surface on which models are evaluated, compared, and chosen.

Problem

• structured output
• answer diversity
• language models
• response format
• output homogeneity

Innovation

• structured output
• answer diversity collapse
• output register
• format-induced convergence
• language model homogenization