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The Price of Format: Diversity Collapse in LLMs

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Full chat templates with role markers and special tokens substantially reduce semantic and topical diversity in LLM outputs, an effect that persists under high temperature and explicit creativity prompts.

desk verdict Chat templates reliably shrink output diversity in open-ended generation, but the paper's universal claim outruns its own point estimates, and the fake-template control doesn't fully isolate structure from system-prompt content. read the letter →

arxiv 2505.18949 v1 pith:4N5DHTUH submitted 2025-05-25 cs.CL cs.AIcs.LG

Longfei Yun , Chenyang An , Zilong Wang , Letian Peng , Jingbo Shang This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords diversitycollapsechattemplatespromptformattinginstructiontuningoutputbehavioralanchorsdecodingtemperatureLLMcreativity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the chat-style formatting used by instruction-tuned LLMs—role markers, special tokens, dialogue scaffolding—actively suppresses the variety of what the model will say, a phenomenon the authors call “diversity collapse.” Across five model families and nine open-ended tasks, full chat templates consistently yield lower semantic and topical diversity than minimal “simple steer” prompts, and the gap persists under high-temperature sampling and even when the prompt explicitly asks for creativity. A controlled ablation shows that removing structural tokens, not just replacing them with meaningless ones, is what restores diversity; even plain-text role labels (“user:”, “assistant:”) still clamp the output space. The paper also shows, by fine-tuning the same model under different formats, that format consistency between tuning and inference is what matters for structure-sensitive benchmarks like GSM8K and IFEval, while knowledge-heavy tasks are largely format-insensitive. The upshot is a tradeoff: the very scaffolding that makes models reliable and aligned also narrows their expressive range, calling for diversity-aware prompt design.

What carries the argument

The mechanism is the “behavioral anchor”: structural tokens such as <|user|> and <|assistant|> that the model saw throughout instruction tuning act as strong priors that compress the conditional output distribution, lowering token-level entropy in the first decoding steps and steering generation onto a narrow path. The paper isolates this by ablating prompts in four steps—full template, fake template, minimal dialog, and simple steer—which lets it separate the effect of structural layout from the semantics of the tokens themselves. The entropy trace across generation steps (Section 4.2) is the direct evidence that the collapse is present early and persists, rather than emerging from later decoding choices.

What would settle it

Take one model and compare four prompts: the full chat template, the same template with the system prompt deleted (keeping role markers), the same with the system prompt replaced by neutral content, and the same with role markers replaced by semantically meaningless tokens. If deleting or neutralising the system prompt restores diversity to the simple-steer level, the structural-anchor explanation is falsified; if the gap persists, the structural tokens themselves are the cause.

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Extended reading notes

Core claim

The central claim is that the structural tokens of a chat template function as behavioral anchors: they lower the entropy of the model’s output distribution from the earliest decoding steps, committing generation to a narrow trajectory that semantic diversity measures show to be far less varied than the same instruction delivered with no formatting. The evidence comes from a consistent ordering of four prompt modes—full template, fake template (structure with meaningless tokens), minimal dialog (plain-text role labels), and simple steer (no structure)—where diversity rises monotonically as structural markers vanish. The effect is not a decoding artifact: raising temperature increases diversity for all prompts, but templates mute the gain, and adding “be creative” instructions inside the template cannot close the gap to simple prompts. Fine-tuning experiments add a second finding: instruction tuning improves response quality and instruction following, but output diversity is governed overwhelmingly by the presence or absence of structural tokens at inference, not by the format used in training.

Load-bearing premise

The load-bearing premise is that the diversity gap is caused by the structural tokens themselves, but in several models (for example Qwen) the Full Template also injects a substantive system prompt, such as “You are Qwen, created by Alibaba Cloud. You are a helpful assistant.”, and the Fake Template control preserves that same content, so the experiments never fully separate structural markers from system-prompt semantics.

Editorial extensions

If this is right

  • If the claim is right, evaluations of an instruction-tuned model’s creative range must control prompt format, because low diversity can be an artifact of the chat scaffold rather than a property of the model.
  • Format consistency between fine-tuning and inference is the dominant factor on structure-sensitive benchmarks such as GSM8K and IFEval, while knowledge-heavy tasks such as MMLU and WebQuestions gain little from templating and may be hurt by it.
  • Removing all structural markers, including plain-text role labels, is the most reliable way to restore output variety; “minimal dialog” formats still carry the anchoring effect.
  • Because templates mute the diversity gains of higher temperature, sampling more aggressively cannot compensate for formatting-induced collapse.
  • Explicitly asking the model to be creative inside a template yields only partial recovery, so surface-level prompting cannot replace removing the structural scaffolding.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • One implication the authors leave implicit is that format dropout during instruction tuning—mixing a substantial share of structure-free samples into the data—might loosen the anchor; their Mixed Training variant added only one-third unstructured data and showed only marginal gains, suggesting the dose or timing matters.
  • A natural extension is to check whether the anchoring generalises to other scaffolds such as chain-of-thought or retrieval wrappers; the paper’s own limitations section notes only chat-style and simple templates were compared.
  • The entropy trace implies a screening tool: measuring the entropy of the first few decoding steps after a role marker should predict how much a template will collapse diversity, without generating full outputs.
  • If behavioral anchoring is the cause, model families tuned with more heterogeneous template distributions should exhibit weaker collapse; comparing diversity across model versions whose pretraining data differed in template uniformity would test that.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper reports an empirical study of how prompt formatting affects the output diversity of instruction-tuned LLMs. The authors compare a Full Template condition (chat-style prompts with special tokens, role markers, and system messages) against a Simple Steer condition (a bare instruction) across five models and nine open-ended tasks, using sentence-embedding distances and entity-based entropy as diversity metrics. They report that simple steer prompts generally yield higher diversity, that the effect persists at high temperature, that structured prompts reduce entropy in early decoding steps, and that explicitly asking the model to be creative does not close the diversity gap. The paper also fine-tunes a Llama-3.2-3B model under several train/inference format combinations and evaluates downstream task performance, alignment quality, and diversity, concluding that format consistency matters for structure-sensitive tasks while structural tokens are the primary driver of diversity collapse.

Significance. The central, falsifiable measurement—that full chat templates reduce output diversity relative to structure-free prompts across models and task types—is practically important for creative-generation applications and for understanding instruction-tuned model behavior. The paper covers multiple architectures, multiple tasks, complementary metrics, and releases code, which are strengths. If the effect withstands statistical scrutiny and the system-prompt confound is resolved, the paper would make a useful contribution to prompt-design and instruction-tuning research. The 'behavioral anchor' explanation is plausible but interpretive; the current evidence does not decisively separate structural tokens from other content differences between the prompt conditions.

major comments (4)
  1. [§4.1, Table 1] The claim that 'full chat templates significantly reduce output diversity compared to simple steer prompts' is not supported by the reported numbers. Four of the 45 model–task cells in Table 1 show the opposite direction (Llama-3-8B-Instruct CommonGen: 0.2884 vs 0.2692; Tulu-3-8B-SFT News: 3.8673 vs 3.7306; Qwen2.5-7B-Instruct News: 0.1200 vs 0.1090; Qwen2.5-7B-Instruct Books: 4.2973 vs 4.0948), and no standard errors, confidence intervals, repeated-seed variance, or significance tests are given for any cell, so the remaining 41 directional cells cannot be distinguished from sampling noise. Please report per-prompt distributions with bootstrap confidence intervals or mixed-effect models, and either demonstrate that the reversals are noise or qualify the universality claim.
  2. [§4.2, Table 6] The key ablation intended to isolate structural markers from token identities is confounded by system-prompt content for Qwen. In Table 6, the Qwen full template includes 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.', and the fake template retains the same semantic system message, whereas Simple Steer and Minimal Dialog do not. Since the comparison of fake template to simpler formats varies both structural markers and the presence/meaning of the system prompt, the conclusion that diversity collapse is driven 'by structural conventions more broadly' is not yet established. Please add a condition that keeps system-prompt wording constant across formats, or remove it from the full template while retaining special tokens, to separate structural effects from system-prompt semantics.
  3. [§5.5, Table 5] Section 5.5 claims that explicitly prompting for diversity 'still fails to close the gap with Simple Steer' across most tasks, but Table 5 contains clear counterexamples: in Travel Recommendation, Full Template w/ Diversity achieves entropy 3.5087 versus Simple Steer's 3.1312, exceeding it, and in CommonGen the values are effectively tied (0.3668 vs 0.3680). The wording 'consistent' and 'across most tasks' needs correction, and the discussion should quantify exceptions. This is load-bearing because the abstract and conclusions use the failure of explicit creativity prompting to argue that structural constraints cannot be mitigated by surface-level prompting.
  4. [§5.2, Table 3] The claim that 'Mixed Template ... consistently underperforms compared to other prompting strategies across nearly all tasks' is overstated. In Table 3, Mixed Template exceeds Full Template on News (1.9646 vs 1.5689) and Travel (4.7086 vs 4.2083), and is close to Natural Instruction on Travel (4.7086 vs 4.7115), with no variance or significance information. Please either provide error bars and a formal comparison, or weaken the inference to 'no meaningful overall improvement' rather than 'consistently underperforms'.
minor comments (6)
  1. [Table 3] The header contains a typo: 'Natrual Instruction' should be 'Natural Instruction'.
  2. [Abstract and §4.1] The word 'significantly' is used in a statistical sense without any statistical test; consider replacing with 'substantially' or adding the necessary significance analysis.
  3. [Figure 5] Figure 5 includes Qwen-3-8B while Table 1 uses Qwen2.5-7B-Instruct; please clarify the exact model version in the caption and indicate whether the figure uses a different checkpoint.
  4. [§5.5] Table 5 refers to a 'Llama-3b model' without specifying the version; the fine-tuning section (§5.1) uses LLAMA-3.2-3B, so please make the connection explicit.
  5. [References] There are formatting glitches in the reference list, such as 'V olume' in the Tevet and Berant entry; please run a consistency pass over all references.
  6. [Table 5] The green/red color coding for 'comparable or better' versus 'limited improvement' is not reproducible in plain text; consider adding explicit symbols or a separate column to encode the comparison outcome.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the diversity-collapse claim is an empirical measurement against external benchmarks, and the only self-citation is non-load-bearing related work.

full rationale

The paper's central claim is an empirical comparison between two prompting conditions: it generates model outputs under Simple Steer and Full Template prompts and measures semantic diversity, label entropy, and lexical diversity using standard external benchmarks and metrics. No parameter is fitted to the target outcome and then re-reported as a prediction, and no equation in the paper defines the conclusion in terms of its inputs. The 'behavioral anchor' explanation in Section 4.2 is a post-hoc interpretation of measured decoding-step entropy, not a derivation that assumes what it concludes. The only self-citation, UltraBench (Yun et al., 2025), appears in the related-work section and is not used to justify the diversity-collapse result, so it is not load-bearing. The internal reversals visible in Table 1, such as Qwen2.5 on News and Travel, are concerns about statistical robustness and the strength of the 'consistent' claim, but they do not make the derivation circular. Overall, the paper is self-contained empirical work with no circular derivation chain.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters are fitted to data; the analysis is empirical. The main assumptions are measurement-based (embedding and GPT-4o label validity) and the representativeness of the tested prompt formats.

assumptions (4)
  • domain assumption Sentence-embedding cosine distance is a valid proxy for semantic diversity.
    Invoked in Section 3 to define the diversity score; prior work is cited, but the choice of embedding model (all-MiniLM-L6-v2) and cosine distance is not validated for the specific tasks here.
  • domain assumption GPT-4o entity extraction returns accurate topic labels for entropy computation.
    Section 3 uses GPT-4o to extract a single entity per generation; no extraction prompt or validation accuracy is reported.
  • domain assumption The off-the-shelf instruction-tuned models were trained with chat templates equivalent to the 'Full Template' used here.
    Section 4.1 states 'we fix the SFT data' for Table 1, but the SFT data for these public checkpoints is not controlled; the claim that inference with the native chat template matches training format is assumed.
  • ad hoc to paper The 'Simple Steer' format is representative of all structure-free prompting.
    Section 4.2 concludes 'Only fully structure-free prompting reliably restores expressive flexibility' based on a single bare-instruction variant; other minimal formats are not tested.

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Cite this review

Pith. "Pith review of The Price of Format: Diversity Collapse in LLMs." pith.science (2026). https://pith.science/paper/4N5DHTUH

@misc{pith2026250518949,
  author       = {Pith},
  title        = {Pith review of: The Price of Format: Diversity Collapse in LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4N5DHTUH}},
  note         = {Machine review of arXiv:2505.18949}
}
read the original 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 the same model 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.

Figures

Figures reproduced from arXiv: 2505.18949 by the authors.

Figure 1
Figure 1. News generation results under simple prompt [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Semantic diversity comparison across Qwen3 (Team, 2025) model sizes under two prompting modes, excluding the thinking mode. The results show that diversity collapse occurs consistently across model scales. constrain not only what models say, but also how they say it, reducing variability in form and content, and thereby narrowing the expressive space. 4.2 Dissecting Template-Induced Collapse Chat Templates as Behavi… view at source ↗
Figure 3
Figure 3. Structural diversity across prompting modes in the news generation task, measured by the standard [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Entropy of the output space across decoding [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Performance comparison across prompting modes ( [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Comparison of win rates on AlpacaEval under [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Effect of decoding temperature on semantic [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. More Is Not More: What Matters for Diversity in LLM Opinions?

    cs.CL 2026-05 conditional novelty 7.0 of 10

    Diversity in LLM opinions comes mostly from the first persona sentence and from combining different interaction architectures, not from richer personas, temperature, or diversity instructions.

  2. When Reasoning Narrows the Move: Diversity Collapse in LLM Game Play

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Supervised fine-tuning collapses LLM action diversity in board-game play beyond what the accuracy–diversity tradeoff requires; augmenting SFT data with all optimal actions per state partially prevents this.

  3. Outcome-based Exploration for LLM Reasoning

    cs.LG 2025-09 conditional novelty 6.0 of 10

    Outcome-based exploration bonuses (UCB-Con and Batch) improve pass@1 and pass@32 for LLM math reasoning while slowing diversity collapse, supported by a bandit model with a strong generalization assumption.

  4. Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software

    cs.CR 2026-02 reject novelty 5.0 of 10

    Frontend features of LLM-generated apps can predict hidden backend vulnerabilities that a given model tends to reproduce, enabling black-box attack triage.

Reference graph

Works this paper leans on

14 extracted references · 10 canonical work pages · cited by 4 Pith papers

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    minimum.dialog: strips system messages and role tokens, retaining only natural language cues (e.g., user: / assistant:)

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    All the prompts are shown in Table 6

    simple steer: removes all structural elements, reducing the prompt to a bare instruction with- out any dialog framing. All the prompts are shown in Table 6. B Traditional Metrics Even when evaluated with traditional n-gram based diversity metrics instead of embedding-based se- mantic evaluations, we observe a consistent ad- vantage in diversity for prompt...

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    IFEval:We evaluate instruction-following capabilities using the IFEval benchmark (google/IFEval). Each example consists of 12 Model Mode Prompt LLaMA full template<|begin of text|><|start header id|>user<|end header id|> Please write a news about a random topic.<|eot id|><|start header id|>assistant<|end header id|> fake template<#init seq><@user name>use...

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    GSM8K:We use the gsm8k/main dataset in free-form generation mode (generate until) with a deterministic decoding setting (tem- perature 0.0). The model is prompted with five few-shot examples (num fewshot=5), and predictions are evaluated using an exact match metric after applying a flexible-extract filter to extract the final numerical answer

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    Final performance is aggregated by dataset size to reflect a balanced view across subjects

    MMLU:We include four subject groups un- der the mmlu group (mmlu stem, mmlu other, mmlu social sciences, mmlu humanities) and compute accuracy (acc) as the evaluation metric. Final performance is aggregated by dataset size to reflect a balanced view across subjects

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    HumanEval: We evaluate the model’s ability to generate correct Python code using the ope- nai/openai humaneval dataset. The generation is truncated on common code delimiters (e.g., \n class , \n def ) and evaluated with the pass@1 metric, which measures the fraction of problem...

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    Each question is formatted as Question: <question>\n Answer :, and the model se- lects one answer from a predefined list of candidates

    Web QS: We evaluate open-domain factual QA using the web questions dataset, fol- lowing the multiple-choice evaluation proto- col. Each question is formatted as Question: <question>\n Answer :, and the model se- lects one answer from a predefined list of candidates. The metric...

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    Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, and Dong Yu

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