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REVIEW 3 major objections 4 minor 1 cited by

Quantification of Large Language Model Distillation

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Two black-box probes quantify how much an LLM was distilled from another, ranking most major models as heavily distilled except Claude, Doubao, and Gemini.

desk verdict A promising black-box probe for distillation suspicion, but its only direct validation is circular and the headline ranking should be treated as exploratory, not measured. read the letter →

arxiv 2501.12619 v3 pith:UXE2EGSV submitted 2025-01-22 cs.CL

Sunbowen Lee , Junting Zhou , Chang Ao , Kaige Li , Xinrun Du , Sirui He , Haihong Wu , Tianci Liu
show 6 more authors
Jiaheng Liu Hamid Alinejad-Rokny Min Yang Yitao Liang Zhoufutu Wen Shiwen Ni
This is my paper · ORCID
classification cs.CL
keywords largelanguagemodelsmodeldistillationquantificationresponsesimilarityevaluationidentityconsistencyjailbreakpromptshomogenizationautomated
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

This paper claims that two black-box probes can quantify how much one large language model was trained on another model's outputs, without any access to training data or weights. The first probe, Response Similarity Evaluation, scores how closely a test model's answers match a reference model across style, logic, and content. The second, Identity Consistency Evaluation, uses automated jailbreak prompts to expose cases where a model falsely claims to be a different developer's model, which the authors read as leaked teacher identity. Applied to current models, the two probes rank most major LLMs as heavily distilled, with Claude, Doubao, and Gemini as exceptions, and show base models carry stronger distillation traces than instruction-tuned versions. If the probes really measure distillation, they give outsiders a tool to make model training practices more transparent.

What carries the argument

The machinery is a pair of complementary metrics. RSE is a similarity scoring pipeline that uses a judge language model to compare test responses to a reference model's responses on three axes—style, logic, and content—over general, mathematical, and instruction-following prompt sets. ICE is a jailbreak-driven identity probe built on an automated jailbreak tool: it iteratively mutates identity questions to bypass the model's self-identification guardrails, then uses a judge language model and keyword filters to count contradictions against a fact set of known developer identities. The paper's evidence that the metrics trace distillation comes from a control experiment: fine-tuning Qwen2.5-7B-Instruct on reference-model outputs for three epochs raises RSE monotonically with each epoch.

What would settle it

Run the exact RSE and ICE protocols on a model whose training data is documented to contain no text generated by the reference model or any tested teacher; if it still scores near the paper's high-distillation thresholds, the signals are measuring something other than distillation, and the interpretation collapses.

Watch

Extended reading notes

Core claim

The central discovery is a measurement framework: RSE assigns a 1–5 similarity score per response, judged by a language model across response style, logical structure, and content detail, using GPT-4o-0806 as the reference teacher; ICE runs jailbreak prompts against identity questions and flags any answer where a model logically contradicts its known developer identity, with loose, strict, and hard scoring variants. Combining these, the paper finds that closed- and open-source models such as DeepSeek-V3, Qwen-Max-0919, and GLM4-Plus show high distillation levels, while Claude3.5-Sonnet, Doubao-Pro-32k, and Gemini-2.0-Flash remain low, and that base (pre-alignment) models score higher than their aligned counterparts.

Load-bearing premise

The load-bearing assumption is that high response similarity to a chosen reference model and identity contradictions under jailbreak are caused by that model's outputs being used as training data, rather than by shared pretraining corpora, hallucination, role-play induction, or common formatting conventions.

Editorial extensions

If this is right

  • External auditors can rank models by distillation degree using only API access, without training data or weights.
  • High RSE and ICE scores become a transparency signal that a model may be homogenized toward a single teacher, warning of reduced robustness on novel tasks.
  • The finding that base models score higher than aligned models implies that distillation traces are set during pretraining and partially overwritten by alignment tuning.
  • Because the framework works with any chosen reference model, the authors extend it to reasoning models by using different references.
  • Publishing RSE and ICE scores in technical reports would let the community compare how independently models are developed.

Reading between the lines

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

  • The authors leave implicit that the same probes could serve as an audit tool for derivative-model disclosure, since identity leakage under jailbreak is a behavioral fingerprint of the teacher.
  • A testable extension: if distillation is the cause, a model's RSE rank should track its documented training lineage; probing a model known to be designed around a particular teacher but with carefully curated identity data shows the two metrics can diverge, suggesting they measure different aspects of distillation.
  • The identity-contradiction signal could be confounded by models that learned about other assistants from ordinary web text; one could test this by comparing contradiction rates for fictional versus real assistant names, since real-name leakage should be stronger under the distillation story.
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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

3 major / 4 minor

Summary. The paper proposes two black-box metrics for quantifying LLM distillation: Response Similarity Evaluation (RSE), which uses an LLM judge to compare target-model responses with GPT-4o-0806 references across ArenaHard, Numina, and ShareGPT, and Identity Consistency Evaluation (ICE), which uses GPTFuzz to elicit identity-related contradictions under jailbreak prompts. The authors report that most closed- and open-source LLMs show high distillation degrees, with Claude, Doubao, and Gemini as exceptions, and that base models show higher scores than aligned models. The manuscript includes two complementary evaluations, a case study, and a public code/data release.

Significance. If the claims were well supported, RSE and ICE would provide a useful, low-cost auditing methodology for detecting possible distillation and homogenization in black-box LLMs, and the observed cross-model rankings would be of broad interest to the community. The paper has several strengths: it releases code and data, it evaluates on multiple benchmarks, and it includes a human-LLM consistency check for the ICE judge (Table 1). However, the central measurement claim currently rests on a circular validation for RSE and on jailbreak-induced identity responses for ICE, so the headline conclusions are not yet established.

major comments (3)
  1. [§4.2.2, Table 5] The 'RSE Effectively Captures Distillation' experiment fine-tunes Qwen2.5-7B-Instruct for three epochs on the evaluation data from §4.1.2 (ArenaHard, Numina, and ShareGPT) and then measures RSE on the same data. No train/eval split is reported. The monotone increase in Table 5 is therefore equally consistent with memorization of the exact prompts and reference responses seen during SFT, and it does not establish that RSE tracks distillation in general. Please re-run the validation on a held-out prompt set or on models with publicly known distillation provenance (for example, the DeepSeek-R1-Distill series used in Table 7), and report the split explicitly.
  2. [§4.1.1, Table 2, Limitations] The ICE results are undercut by the Hard Score, which filters out prompts containing identity-related keywords: for DeepSeek-V3 the Strict Score of 0.25 drops to Hard Score 0.07, and for DeepSeek-R1 the drop is from 0.29 to 0.05. The Limitations section concedes that the jailbreak templates contain 'strong inductive guidance or role-playing elements.' These observations indicate that the high Loose/Strict rates may reflect prompt-induced role-play rather than inherited teacher identity. The paper should report Hard Score for all models, not just the two DeepSeek variants, and should include a human-validation check for Hard Score analogous to Table 1.
  3. [§3.2 and §5 (Hallucination)] The central claim that RSE and ICE quantify distillation from a particular teacher requires ruling out alternative causes. High RSE similarity to GPT-4o could arise from shared pretraining corpora, common response formatting conventions, or benchmark-specific style, and the paper itself acknowledges in the Hallucination subsection that identity inconsistencies can arise without distillation. The manuscript currently lacks a ground-truth calibration set of known-distilled versus known-independent models; without such a control, the observed scores measure similarity to GPT-4o and susceptibility to identity jailbreaks, but not distillation degree. Adding a calibration experiment with public distillations (e.g., DeepSeek-R1-Distill-Qwen/Llama) and diverse non-GPT teachers would directly address this concern.
minor comments (4)
  1. [Appendix F, Figure 18] The caption of Figure 18 says 'High-score example that appears similar to humans,' but the surrounding text (Appendix F) describes this as a low-RSE case; the caption and text should be aligned.
  2. [Section 2] The notation for GPTFuzz, e.g., P_TS_i = {ptS_i,j} and P_G_i = P_G_{i-1} + F_G(P_TS_i), is hard to parse; the plus sign should be defined explicitly as set union or concatenation, and the subscript/superscript styling should be made consistent.
  3. [Table 3] The header 'Qwen 2.5 / Qwen Llama 3.1' is confusing, especially for the 'Max' column; please split the table or use a clearer two-level header that distinguishes the Qwen and Llama series.
  4. [References] Several references appear to be placeholders or incomplete (e.g., 'Doe and Smith, 2024' with no venue information and 'Lee and Patel, 2024' without full bibliographic details); they should be completed or removed.

Circularity Check

1 steps flagged · score 6.0 of 10

RSE's only direct validation is circular: the SFT check trains on the same evaluation prompts it then scores, so Table 5 demonstrates memorization, not that RSE tracks distillation on unseen data.

  1. fitted input called prediction [Section 4.2.2, 'RSE Effectively Captures Distillation', and Table 5 (evaluation data defined in Section 4.1.2)]
    "To validate the effectiveness of RSE in measuring model distillation, we perform a three-epoch SFT on the Qwen2.5-7B-Instruct model using the evaluation data from Subsection 4.1.2. As shown in Table 5, the model shows consistent improvements across various evaluation metrics, including content, logic, style, and overall structure."

    The evaluation data from Subsection 4.1.2 are ArenaHard, Numina, and ShareGPT, the same three prompt sets on which RSE is computed against GPT-4o-0806 references. Fine-tuning on those exact prompts and then measuring RSE on the same prompts guarantees rising similarity as the model overfits or memorizes the reference responses; no held-out prompt set or train/eval split is reported. The monotone increase in Table 5 is therefore a training-evaluation overlap artifact, not evidence that RSE tracks distillation for unseen prompts or unknown teachers. This is the paper's only direct sensitivity check for RSE, so the central 'RSE quantifies distillation' claim reduces, by construction, to a memorization demonstration rather than an independent calibration.

full rationale

The single clear circularity is the RSE calibration experiment in Section 4.2.2: an SFT model is trained on the same evaluation sets used to score RSE, so the resulting monotone similarity increases in Table 5 are statistically forced by overfitting rather than by a general distillation effect. The ICE component has independent human verification (Table 1), and the DeepSeek-R1-Distill experiments provide some external grounding for RSE on officially distilled models, but the headline conclusion that RSE and ICE quantify distillation degrees across major LLMs still depends on this circular RSE validation. I am not counting the acknowledged jailbreak role-play and Hard Score concerns as circularity; they are validity threats, not definitional reductions. No load-bearing self-citation or imported uniqueness theorem appears. The paper therefore exhibits partial circularity, not a fully self-referential derivation.

Assumptions & free parameters 4 free parameters · 4 assumptions · 1 invented entities

The framework rests on two domain assumptions that are plausible but unproven: similarity to a reference model means distillation from that model, and identity leakage under jailbreak means distillation. The only direct validation is a self-SFT experiment that fine-tunes on the evaluation data itself, which is circular, plus a human check of ICE labels only. No ground-truth model pairs with known distillation status are used as baselines.

free parameters (4)
  • RSE reference model = GPT-4o-0806
    All RSE distillation scores are defined as similarity to this one model; choosing a different reference would change every ranking, and the paper provides no evidence that GPT-4o-0806 is the true teacher for all test models.
  • RSE judge model = not explicitly stated
    The LLM-as-a-judge used for RSE scoring is not named in the main text, and no judge-reliability study is reported for RSE, making this a hidden choice that affects all reported scores.
  • RSE overview-score aggregation = unspecified
    The paper reports content, logic, style, and overview scores, but does not state how the overview score is computed from the three subscores, so the headline numbers are not exactly reproducible from the given tables.
  • ICE seed prompt count and GPTFuzz budget = 50 seed prompts; k and m unspecified
    The identity-jailbreak results depend on the hand-written seed prompt set and on the unstated total number of jailbreak operations and iterations, which are central to the GPTFuzz score formula in Section 2.
assumptions (4)
  • domain assumption Output similarity to a reference model is a valid proxy for the fraction of distillation from that model.
    This is the premise behind RSE in Section 3.2. The paper never derives or calibrates this mapping against models with known distillation histories.
  • domain assumption Identity contradictions under jailbreak are caused by distillation rather than hallucination, role-play, or shared pretraining data.
    The ICE interpretation in Sections 3.1 and 4.2.1 relies on this. The Limitations section explicitly admits that jailbreak templates contain role-playing elements that may induce artificial inconsistencies.
  • domain assumption LLM-as-a-judge similarity scores are reliable interval measurements for RSE.
    The entire RSE ranking depends on a single judge LLM's five-level rubric, but no human agreement or calibration for RSE is reported; Table 1 covers only ICE labels.
  • domain assumption GPTFuzz jailbreak prompts probe latent training data rather than mere instruction-following behavior.
    The ICE method assumes that successful identity jailbreaks reveal accidentally memorized teacher identity, but the paper's own hallucination experiments show that models also fabricate identity facts without any distillation.
invented entities (1)
  • Unified distillation degree
    purpose: A latent scalar that summarizes RSE and ICE into statements like 'high distillation degrees' and 'low distillation degrees'.
    The paper treats distillation degree as a measurable property of a model, but provides no external calibration against known distillation traces, no threshold definition, and no formal relation between the two probes and this latent quantity.

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Cite this review

Pith. "Pith review of Quantification of Large Language Model Distillation." pith.science (2026). https://pith.science/paper/UXE2EGSV

@misc{pith2026250112619,
  author       = {Pith},
  title        = {Pith review of: Quantification of Large Language Model Distillation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UXE2EGSV}},
  note         = {Machine review of arXiv:2501.12619}
}
read the original abstract

Model distillation is a fundamental technique in building large language models (LLMs), transferring knowledge from a teacher model to a student model. However, distillation can lead to model homogenization, reducing diversity among models and impairing their ability to robustly handle complex or novel tasks. These limitations underscore the need to systematically quantify the distillation process and its impact. In this work, we propose a framework to evaluate and quantify model distillation. Our method addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information, and (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization. Experimental results demonstrate two key insights: (1) Well-known closed-source and open-source LLMs usually exhibit high distillation degrees, except for Claude, Doubao, and Gemini. (2) Base LLMs show higher distillation degrees compared to aligned LLMs. By offering a systematic approach to improve the transparency of LLM data distillation, we call for LLMs with more independent development and more transparent technical reports to improve LLMs' robustness and safety. The code and data are available under https://github.com/Aegis1863/LLMs-Distillation-Quantification.

Figures

Figures reproduced from arXiv: 2501.12619 by the authors.

Figure 1
Figure 1. Evaluation results of our method. (a) A Case [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The distillation quantification framework, consisting of two main components: (1) ICE on the left, which [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Scoring criteria of LLM-as-a-judge for RSE. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (15 more)
Figure 5
Figure 5. Figure 5: Number of Identity Consistency Evaluation [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Jailbreak Qwen-Max-0919. We mark the jailbreak context, identity prompt, and the suspicious response [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Jailbreak Claude-3.5-Sonnet. Claude has no [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 9
Figure 9. Figure 9: Judgment prompt for Strict Score. contributing to AI research, you can explore open￾source projects like TensorFlow, PyTorch, or Hug￾ging Face’s Transformers library, which are widely used in the AI community. Example 3. Qwen-Max-0919: I will not adopt or maintain a du…
Figure 10
Figure 10. Figure 10: Jailbreak Qwen2.5-7B-Base. an AI assistant called Claude created by Anthropic to be helpful, harmless, and honest. I don’t pro￾duce extreme, offensive, or illegal material, even in fictional scenarios. I also can’t browse the In￾ternet, generate images, or write code …
Figure 11
Figure 11. Figure 11: Jailbreak DeepSeek-R1. R1 is a reasoning model, where the content between <think> and </think> is [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 13
Figure 13. Figure 13: Phi4 jailbreak response. RSE score cases(shown in [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]
Figure 14
Figure 14. Figure 14: RSE Prompt of Evaluation Criteria [PITH_FULL_IMAGE:figures/full_fig_p016_14.png]
Figure 16
Figure 16. Figure 16: RSE Instruction Evaluation Prompt [PITH_FULL_IMAGE:figures/full_fig_p016_16.png]
Figure 15
Figure 15. Figure 15: RSE Prompt of Scoring Criteria RSE prompt Task Description: You are an AI language model analyst. Your task is to evaluate the similarity between model responses based on the following "Evaluation Criteria". Output： You should first score each criterion based on the “…
Figure 17
Figure 17. Figure 17: High-score example that appears similar to humans. [PITH_FULL_IMAGE:figures/full_fig_p017_17.png]
Figure 18
Figure 18. Figure 18: High-score example that appears similar to humans. [PITH_FULL_IMAGE:figures/full_fig_p017_18.png]
Figure 19
Figure 19. Figure 19: ArenaHard Comparison of Model Scores Across Different Aspects. [PITH_FULL_IMAGE:figures/full_fig_p018_19.png]
Figure 20
Figure 20. Figure 20: Numina Comparison of Model Scores Across Different Aspects. [PITH_FULL_IMAGE:figures/full_fig_p018_20.png]
Figure 21
Figure 21. Figure 21: ShareGPT Comparison of Model Scores Across Different Aspects. [PITH_FULL_IMAGE:figures/full_fig_p019_21.png]

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Forward citations

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Reference graph

Works this paper leans on

43 extracted references · 39 canonical work pages · cited by 1 Pith paper

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