The Price of Format: Diversity Collapse in LLM s (2025)

Abstract: Instruction-tuned large language models (LLMs) employ structured templates, such as role markers and special tokens, to enforce format consistency during inference. However, we identify a critical limitation of such formatting: it induces a phenomenon we term diversity collapse, where the model generates semantically similar outputs for open-ended inputs, undermining creativity and variability. We systematically evaluate this effect across tasks like story completion and free-form generation, finding that (1) diversity collapse persists even under high-temperature sampling, and (2) structural tokens in templates significantly constrain the model’s output space. To contextualize these findings, we fine-tune using a range of structured prompts and then evaluate them across three axes: downstream task performance, alignment behavior, and output diversity. Our analysis shows that format consistency between fine-tuning and inference is crucial for structure-sensitive tasks (e.g., GSM8K, IFEval), but has marginal influence on knowledge-heavy tasks (e.g., MMLU, WebQuestions). In contrast, output diversity is primarily governed by the presence or absence of structural tokens, with minimal formatting yielding the most diverse outputs. These findings reveal that current prompting conventions, while beneficial for alignment, may inadvertently suppress output diversity, underscoring the need for diversity-aware prompt design and instruction tuning.

## introduction instruction-tuned llms commonly adopt structured prompt templates that include role markers such @xmath0, as well as special tokens @xmath1. ## understanding diversity collapse we begin by examining how diversity collapse presents in model outputs ( §4.1), and follow with an analysis of its underlying causes ( §4.2). ## mitigation strategies and further analysis we answer these questions in this section: 1. can structural prompt variants mitigate diversity collapse? ( §5.1, §5.2) 2. how do diversity-preserving prompts affect downstream task performance and instructionfollowing ability? ( §5.3) 3. can higher decoding temperatures ( §5.4) or explicit prompting for creativity restore lost diversity ( §5.5)? ## conclusion in this paper, we identified and investigated the phenomenon of diversity collapse, revealing how structured chat templates significantly reduce semantic and topical diversity in instruction-tuned large language models. through comprehensive empirical analyses and ablation studies, we demonstrated the structural factors causing this limitation and provided practical strategies to mitigate it. our findings highlight the crucial role of prompt design in preserving model creativity, offering valuable insights for future research and applications of language models. our work is sponsored in part by nsf career award 2239440, nsf proto-okn award 2333790, sponsored research projects from companies like cisco and ebay, as well as generous gifts from google, adobe, and teradata. any opinions, findings, and conclusions or recommendations expressed herein are those of the authors and should not be interpreted as necessarily representing the views, either expressed or implied, of the u.s. government. the u.s. government is authorized to reproduce and distribute reprints for government purposes not withstanding any copyright annotation hereon.