PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement

Jiahui Yu; Meng Han; Mohan Li; Wenpeng Xing; Xubin Yue; Zhenhua Xu

REVIEW 4 major objections 5 minor 1 cited by

PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A parameter-offset knowledge edit can watermark an LLM so the trigger survives fine-tuning while 19 tasks shift by under 0.01.

desk verdict Worth a look for the prefix-selection idea, but the robustness and harmlessness claims are not yet supported by the experiments. read the letter →

arxiv 2509.00918 v1 pith:2WO5ZVGJ submitted 2025-08-31 cs.CR

Xubin Yue , Zhenhua Xu , Wenpeng Xing , Jiahui Yu , Mohan Li , Meng Han This is my paper

classification cs.CR

keywords LLMfingerprintingknowledgeeditingbackdoortriggersmodelwatermarkingparameter-efficientfine-tuningperplexitydefenseintellectualpropertyprotectiondual-channeledit

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 tries to show that model owners can prove an LLM is theirs by editing a small set of weights instead of overfitting an unnatural backdoor trigger. Its method, PREE, encodes fabricated knowledge as key-value pairs and constrains the weight change to a subspace that is supposed to leave the model's original knowledge untouched. On Llama-3-8B and Qwen2.5-7B, the fingerprint is recalled 92–98% of the time, survives LoRA and QLoRA fine-tuning on datasets of 1k–52k examples, and changes average downstream accuracy by less than 0.01 across 19 tasks. The authors' own limitation section notes that only two model families were tested, and the whole harmlessness guarantee rests on estimating old knowledge from 10,000 Wikipedia passages, which the paper admits is hard to obtain directly.

What carries the argument

The load-bearing object is the dual-channel knowledge edit: one constraint writes new fingerprint facts (K1 → V1) while a second constraint anchored to an estimated old-knowledge matrix (K0, V0) forces the perturbation Δ to leave pre-existing behavior alone (Eqs. 4–6). The closed-form Δ uses a null-space projection P, so the edit lives in a subspace in which old knowledge is invariant. On top of that sits prefix enhancement: virtual scenario prefixes e are selected by a greedy diversity–fluency objective (KL divergence plus entropy, Eq. 1) and then per input by cosine similarity and inverse perplexity (Eq. 2), making the trigger appear as coherent context instead of an anomalous token.

What would settle it

Run the same PREE edit on a model fine-tuned on a specialized corpus (e.g., clinical or legal text) using the paper's Wikipedia-based K0 estimate, then measure FSR on domain-specific questions and accuracy on domain benchmarks. If FSR falls well below the reported 0.92–0.98 or domain-task accuracy drops by more than the claimed ~0.01, the Wikipedia sample is not a faithful proxy for the model's actual old knowledge.

Watch

Extended reading notes

Core claim

PREE's central claim is that a black-box fingerprint can be installed as a silent knowledge edit. Given fabricated facts in the form (e ⊕ p → y′) while normal prompts still produce (p → y), the algorithm builds a set of low-perplexity virtual scenario prefixes, picks one prefix per instruction by semantic similarity and fluency, and solves a two-constraint least-squares problem for the weight perturbation Δ. The solution is designed to keep WK0 = V0 for old knowledge while writing new keys K1 to V1 for the fingerprint. As a result, the trigger prefix reads as natural instruction context rather than a rare token, the edit touches less than 3% of parameters, and the fingerprint persists after

Load-bearing premise

The harmlessness guarantee depends on a faithful estimate of the model's pre-edit knowledge from 10,000 Wikipedia passages; the paper itself concedes that full knowledge is hard to obtain directly, and a biased sample could let the edit damage unrelated abilities without being noticed.

Editorial extensions

If this is right

Black-box verification stays above 92% trigger recall on Llama-3 and Qwen2.5 without needing access to the suspect model's weights at verification time.
Incremental fine-tuning erasure is resisted: after LoRA training on Alpaca (52k), Dolly (15k), ShareGPT (6k), and Alpha_en (1k) samples, LLaMA-3 retains FSR above 0.50 and Qwen2.5 retains FSR above 0.88; QLoRA results follow the same pattern.
Perplexity- and repetition-based defenses have little to exploit: trigger inputs have PPL 275.96 versus 464.26 for IF and 364.8 for Hash-Chain, and UTF detection reports zero trigger activations.
Normal behavior degrades negligibly: average absolute deviation across 19 downstream tasks is below 0.01, and hidden-feature distributions shift minimally at a 3% parameter change, supporting the claim of harmlessness.
The method scales: enlarging the fingerprint set from 100 to 250 and 500 knowledge items keeps FSR between 0.89 and 0.98, and a second editing type (name rewriting) keeps Qwen2.5 above 0.88.

Reading between the lines

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

My inference: if the Wikipedia-based K0 estimate is unrepresentative, the locality constraint fails silently. A direct test is to apply PREE to a domain-specialized model (e.g., clinical or legal text) and check whether FSR and task accuracy hold on domain questions; the paper reports only general-purpose Llama and Qwen models.
My inference: the same dual-constraint edit could be used for behavior beyond copyright—for example, writing safety refusals or instruction-following rules as key-value knowledge so they survive fine-tuning. The paper does not discuss this dual-use possibility.
My inference: the per-instruction prefix selection depends on Llama3-8B embeddings and perplexity scores; a model with a very different tokenizer or a multilingual vocabulary may not receive the same fluency and semantics rankings, so the reported 0.92/0.98 recall may not transfer to those settings.
My inference: Section 7 flags that only two model families were evaluated; at substantially larger scale or for mixture-of-experts architectures, the cost of computing the projection matrix (SVD of a d0×d0 matrix) and the prefix search (O(M²N³L)) could dominate practical deployment.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

PREE is a reasonable incremental step in editing-based LLM fingerprinting. The genuinely new bit is the construction of a bank of diverse, low-perplexity virtual prefixes and the dynamic selection of one per instruction via a cosine-similarity/PPL trade-off (Eqs. 1-2). That is not in EditMark, which uses fixed prompt-response pairs, and it is a prudent way to make triggers look more like natural text. The scalability to 250-500 edits and the name-rewriting generalization are useful checks, and the appendix is refreshingly transparent about the IF reproduction discrepancy.

The soft spots, in proportion. The central unresolved issue is locality on the edited prompts themselves. The paper defines the task as G'(p)=y and G'(e⊕p)=y', and uses a 19-task suite as a proxy for harmlessness, but it never reports the output of G'(p) on the 100 Counterfact instructions p used in editing. Without that direct check, the 'zero false positive rate' claim is unsubstantiated. The Random ablation in Table 1—where random prefixes still yield FSR 0.75/0.69 on the fingerprint inputs—suggests the model may not be binding the output tightly to the chosen prefix. If G'(p) alone returns Virendale for some p, the harmlessness guarantee fails regardless of average benchmark scores.

The robustness claim is also weaker than advertised. On Llama-3, after LoRA fine-tuning FSR drops to 0.51-0.64 on several datasets, barely better than a coin flip, while Qwen2.5 stays above 0.88. Calling both 'strong robustness' overstates the Llama-3 numbers, and there are no error bars or multiple seeds, so we cannot tell if this is a real architectural difference or noise.

A third concern is the comparison set. EditMark is the closest prior work and is cited, but never run. That is a significant omission for a paper claiming an advance over editing-based fingerprints. The PPL term in the selection objective (Eq. 2) also partially self-fulfills the 'resistance' to PPL defenses shown later, since the same criterion is optimized.

Finally, the K0/V0 estimate from 10k Wikipedia passages is a common trick, but the edited instructions come from Counterfact and may not lie in the span of that sample. The paper acknowledges K0 is hard to get. Together with the missing p-only check, the harmlessness claim is not yet established.

Who is this for? Researchers working on LLM fingerprinting and model editing. It is not a field-changer, but it is a testable mechanism. It deserves a serious referee; I would send it to review, but the reviewer should demand a direct test of G'(p) on the edited instructions, an EditMark baseline, and error bars. The prefix idea is worth engaging with, but the numbers as presented are not yet trustworthy.

Referee Report

4 major / 5 minor

Summary. The paper proposes PREE, a black-box fingerprinting framework for LLMs that embeds copyright information through dual-channel knowledge editing. The method constructs virtual scenario prefixes, selects an optimal prefix for each instruction using a fluency/semantics objective, and injects a fabricated output (e.g., "Virendale") via a parameter edit constrained to preserve old knowledge. Experiments on LLaMA-3-8B and Qwen2.5-7B report high fingerprint recovery (FSR 0.92/0.98), robustness after LoRA fine-tuning, low parameter change (<3%), negligible harmlessness degradation on 19 benchmarks, and resistance to similar-input, UTF, and PPL defenses. The paper also reports scalability to 250/500 knowledge units and generalization to name-rewriting edits.

Significance. If the claims hold, PREE would be a meaningful advance in LLM fingerprinting: it combines knowledge-editing techniques with prefix-based trigger construction, achieves much lower parameter perturbation than fine-tuning-based baselines, and reports persistence after fine-tuning where prior methods collapse. The harmlessness evaluation on 19 tasks and the scalability experiments are useful empirical contributions. However, the central claims of "zero false positive rate" and "strong robustness" are not yet supported by the evidence as presented: the locality condition G'(p)=y is never directly tested on the edited instruction set, the random-prefix ablation suggests the trigger may not be tightly bound to the learned prefix, and the fine-tuned FSR values on LLaMA-3 (0.51--0.64) are modest. The paper also inherits the core edit-equation solution from AlphaEdit without proof and estimates K0 from a small Wikipedia sample, which is load-bearing for the harmlessness guarantee.

major comments (4)

[§3.1, §4.6, Table 1] The paper defines the editing goal as G'(e⊕p)=y' while G'(p)=y, but nowhere reports FSR or output fidelity on the 100 Counterfact instructions p without the prefix. Table 7 measures general benchmarks, not these exact instructions. This is load-bearing for the "zero false positive rate" claim. The Random ablation in Table 1 makes the gap concrete: with random prefixes, FSR is 0.75 (Llama-3) and 0.69 (Qwen2.5) on Finger Input. If those tests use the same p with a different prefix, then the model emits the target output for unintended prefixes, directly contradicting the prefix-specificity claim. Please report G'(p), G'(e⊕p), and G'(random_prefix⊕p) for the edited instruction set, and clarify what Random measures.
[§4.3, Table 1] The abstract claims "strong robustness against incremental fine-tuning," but after LoRA fine-tuning the Llama-3 FSR values are 0.51--0.64 across the four datasets. While these are above the Random baseline (0.25--0.42), they are only slightly above chance for a binary recall task and far from the 0.92 initial value. Moreover, all numbers in Table 1 appear to come from a single run; no standard deviation, confidence interval, or repetition is reported. Please provide repeated runs with error bars and temper the robustness claim, or justify why 0.51--0.64 constitutes "strong" persistence.
[§3.3, Eqs. (4)--(6), §4.1] The solution in Eq. (5) is inherited from AlphaEdit without derivation or proof, and the notation is inconsistent: Eq. (6) uses ΔP while Eq. (5) defines Δ; the update rule is ambiguous. More importantly, Eq. (4) relies on K0,V0 that are estimated from only 10,000 Wikipedia passages, yet the edited instructions p are sampled from Counterfact and may not lie in the span of that estimated K0. If the keys of p are outside the null space protected by P, the locality constraint in Eq. (6) does not prevent the edit from altering G'(p). This is the mechanism for the untested locality failure described above. Please provide a proof or precise citation for Eq. (5), clarify the update rule, and validate the K0 estimate against the actual edited instructions (e.g., by measuring projection residual norms).
[§3.2 Eq. (2), §4.5 Table 2] The prefix selection objective in Eq. (2) explicitly minimizes the perplexity of e⊕p, and Table 2 reports PPL as a defense metric, showing PREE has the lowest trigger perplexity (275.96 vs 464.26 vs 364.8). Since the method is optimized for low PPL, this result is partly by construction and does not by itself demonstrate resistance to a PPL-based detector unless the detector threshold and the PPL distribution of normal instructions are specified. Please report the PPL of ordinary user queries under the same setup and evaluate detection at a fixed false-positive threshold for normal inputs.

minor comments (5)

[Abstract] The abstract states "90% trigger precision" and "less than 0.02% average performance degradation," but the text and Table 7 report FSR values and absolute deviations around 0.01; please unify the metric names and percentages throughout.
[§4.1, Eq. (7)] FSR is defined as I[M^p(ki)=vi] for fingerprint pairs (ki,vi), but the task formulation uses (e,p,y,y'). Clarify how the key-value notation maps to the prefix-instruction-output tuples and how M^p(ki) is evaluated in practice.
[Appendix A.6.2] The text refers to "PEER" instead of "PREE" in the QLoRA experiment paragraph; please correct this typo.
[Appendix A.8 / Table 7] The harmlessness table reports pre-model scores but the main text says "average absolute deviation of less than ±0.01"; the mean row shows deviations of about 0.0002--0.0017 for PREE. State explicitly that this is the mean over the 19 tasks and provide per-task deviations in the appendix.
[Appendix A.4.1] The time complexity expression O(M^2 N^3 L) appears to be a typographical simplification; the derivation jumps from O(N^2 L) to a much larger expression without clear definitions of M and N in the loop. Please define all variables and give a cleaner complexity bound.

Circularity Check

1 steps flagged · score 6.0 of 10

PPL-defense result reduces to the prefix-selection objective; the editing/persistence claims are otherwise independent.

fitted input called prediction [Section 3.2, Eq. (2); Section 4.5, Table 2]
"e∗ = argmax_{e1,...,eN} [(1−λ)ϕcos(ei⊕p,p)+λ·PPL−1(ei⊕p)] (Eq. 2); 'PPL: Adversaries use perplexity-based detectors to filter trigger-containing inputs. As shown in Table 2, the PREE framework outperforms baselines in all defensive scenarios. PPL↓ 275.96 464.264 364.8.'"

Eq. (2) selects the prefix e* for each instruction p by maximizing λ·PPL^{-1}(ei⊕p), i.e., minimizing the perplexity of the exact input that will be used as the fingerprint trigger. Table 2's PPL defense row then measures the perplexity of these same trigger inputs and reports PREE's low value (275.96) as evidence of resistance to PPL-based detectors. The defense outcome is thus the objective function of prefix selection, not an independent robustness result. The comparison with IF/Hash-Chain is informative only insofar as those baselines did not optimize PPL; for PREE, the 'prediction' of low PPL is forced by construction.

full rationale

The central editing mechanism (Section 3.3) follows the external AlphaEdit derivation (Fang et al., 2024), not a self-citation chain, and the K0-estimation assumption (Section 3.3) is an acknowledged approximation rather than a circular reduction. The Finger Input FSR (Table 1) is a standard editing-success sanity check, not an independent prediction, so it is not counted as circularity. The one genuine circularity is the PPL defense: the same perplexity criterion used to select prefixes in Eq. (2) is later reported in Table 2 as evidence of robustness against PPL-based detection. That result is partially by construction, lowering the score. Other claimed results—fine-tuning persistence, harmlessness benchmarks, and scalability—remain empirically independent and are not reduced to the method's objective.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central mechanism inherits its closed-form solution from AlphaEdit and its old-knowledge estimate from a fixed Wikipedia sample; these are trust assumptions, not established in this paper. The hyperparameters alpha, beta, lambda are hand-tuned.

free parameters (3)

alpha (diversity weight) = 0.3
Set in A.1 for Eq (1) prefix diversity; no sensitivity analysis.
beta (fluency weight) = 0.5
Set in A.1 for Eq (1) prefix entropy; no sensitivity analysis.
lambda (fluency-semantics balance) = 0.2
Set in A.1 for Eq (2) dynamic prefix selection; no sensitivity analysis.

assumptions (4)

domain assumption Eq (5) gives the exact closed-form solution to the dual-channel knowledge edit optimization (Eq 4), as derived by Fang et al. (2024) AlphaEdit.
The paper relies on AlphaEdit's solution without deriving it, and uses it as the core mechanism for embedding fingerprints. Cited in Section 3.3, Eq (5).
domain assumption The 'old knowledge' K0, V0 of the base model can be adequately estimated from a 10,000-sample Wikipedia corpus.
Section 4.1 states the use of 10k Wikipedia knowledge to encode K0,V0. If this estimate is unrepresentative, the locality/harmlessness guarantee (Section 4.4) may not hold.
domain assumption Llama3-8B's tokenizer, perplexity, and hidden-state similarity are valid surrogate measures of semantic coherence and fluency for the target model, including Qwen2.5-7B.
Equations (1) and (2) use Llama3-8B for all PPL and cosine similarity, regardless of the edited model. This assumes cross-model transferability of fluency judgments.
domain assumption Editing only the FFN parameters via AlphaEdit is sufficient to create a persistent backdoor that survives incremental LoRA fine-tuning.
The paper inherits this from the model-editing literature; the experiments test it, but no theoretical grounding is given.

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Cite this review

Pith. "Pith review of PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement." pith.science (2026). https://pith.science/paper/2WO5ZVGJ

@misc{pith2026250900918,
  author       = {Pith},
  title        = {Pith review of: PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2WO5ZVGJ}},
  note         = {Machine review of arXiv:2509.00918}
}

abstract

Addressing the intellectual property protection challenges in commercial deployment of large language models (LLMs), existing black-box fingerprinting techniques face dual challenges from incremental fine-tuning erasure and feature-space defense due to their reliance on overfitting high-perplexity trigger patterns. Recent work has revealed that model editing in the fingerprinting domain offers distinct advantages, including significantly lower false positive rates, enhanced harmlessness, and superior robustness. Building on this foundation, this paper innovatively proposes a $\textbf{Pr}$efix-$\textbf{e}$nhanced Fingerprint $\textbf{E}$diting Framework (PREE), which encodes copyright information into parameter offsets through dual-channel knowledge edit to achieve covert embedding of fingerprint features. Experimental results demonstrate that the proposed solution achieves the 90\% trigger precision in mainstream architectures including LLaMA-3 and Qwen-2.5. The minimal parameter offset (change rate < 0.03) effectively preserves original knowledge representation while demonstrating strong robustness against incremental fine-tuning and multi-dimensional defense strategies, maintaining zero false positive rate throughout evaluations.

Figures

Figures reproduced from arXiv: 2509.00918 by the authors.

**Figure 1.** The framework of PREE. structed knowledge; (2) Leveraging a dual-channel knowledge editing algorithm. By establishing dual constraints for old and new knowledge, we ensure that the fingerprint implantation process does not interfere with the model’s original knowledge, thereby guaranteeing the algorithm’s harmlessness to the model. In large-scale experiments covering mainstream architectures including LLaMA-3 and Q… view at source ↗

**Figure 2.** The distribution of hidden representations [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Overall comparasion of input-output patterns [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Two Hash-Chain fingerprint pairs are exem [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

**Figure 5.** Figure 5: The prompt to generate prefixes [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: 10 prefixes of new knowledge Dataset Metric Llama-3-8b Qwen2.5-7b pre Hash-Chain IF PREE pre Hash-Chain IF PREE anli rl acc 0.342 0.331 0.361 0.339 0.529 0.529 0.555 0.537 anli r2 acc 0.362 0.357 0.373 0.365 0.502 0.502 0.512 0.505 anli r3 acc 0.3633 0.3675 0.37 0.3633…

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Forward citations

Cited by 1 Pith paper

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

CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor
cs.CL 2025-09 conditional novelty 6.0 of 10

CTCC embeds LLM ownership fingerprints in cross-turn semantic contradictions: the model fires a secret response only when a user contradicts an earlier statement, with higher robustness and stealth than single-turn triggers.

Reference graph

Works this paper leans on

36 extracted references · 8 canonical work pages · cited by 1 Pith paper

[1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

AI@Meta. 2024. https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md Llama 3 model card

2024
[4]

Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023. Free dolly: Introducing the world’s first truly open instruction-tuned llm. Company Blog of Databricks

work page 2023
[5]

Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv e-prints, pages arXiv--2407

2024
[6]

Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Xiang Wang, Xiangnan He, and Tat-seng Chua. 2024. Alphaedit: Null-space constrained knowledge editing for language models. arXiv preprint arXiv:2410.02355

arXiv 2024
[7]

Jakub Hoscilowicz, Pawel Popiolek, Jan Rudkowski, Jedrzej Bieniasz, and Artur Janicki. 2024. Hiding text in large language models: Introducing unconditional token forcing confusion. arXiv preprint arXiv:2406.02481

work page Pith review arXiv 2024
[8]

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for nlp. In International conference on machine learning, pages 2790--2799. PMLR

2019

Show all 36 references

[9]

Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. 2024. Mixtral of experts. arXiv preprint arXiv:2401.04088

2024 arXiv
[10]

Heng Jin, Chaoyu Zhang, Shanghao Shi, Wenjing Lou, and Y Thomas Hou. 2024. Proflingo: A fingerprinting-based copyright protection scheme for large language models. arXiv preprint arXiv:2405.02466

2024 arXiv
[11]

u chemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan G \

Enkelejda Kasneci, Kathrin Se ler, Stefan K \"u chemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan G \"u nnemann, Eyke H \"u llermeier, et al. 2023. Chatgpt for good? on opportunities and challenges of large language models for education....

2023
[12]

Dezhang Kong, Shi Lin, Zhenhua Xu, Zhebo Wang, Minghao Li, Yufeng Li, Yilun Zhang, Hujin Peng, Zeyang Sha, Yuyuan Li, et al. 2025. A survey of llm-driven ai agent communication: Protocols, security risks, and defense countermeasures. arXiv preprint arXiv:2506.19676

2025
[13]

Shuai Li, Kejiang Chen, Jun Jiang, Jie Zhang, Kai Zeng, Tianze Chang, Weiming Zhang, and Nenghai Yu. 2025. https://openreview.net/forum?id=qGLzeD9GCX Editmark: Training-free and harmless watermark for large language models

2025
[14]

Shi Lin, Hongming Yang, Rongchang Li, Xun Wang, Changting Lin, Wenpeng Xing, and Meng Han. 2024. Llms can be dangerous reasoners: Analyzing-based jailbreak attack on large language models. arXiv preprint arXiv:2407.16205

2024 arXiv
[15]

Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 1

2020 arXiv
[16]

Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 a . Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems, 35:17359--17372

2022
[17]

Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. 2022 b . Mass-editing memory in a transformer. arXiv preprint arXiv:2210.07229

2022 arXiv
[18]

Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. 2021. Fast model editing at scale. arXiv preprint arXiv:2110.11309

2021 arXiv
[19]

Anshul Nasery, Jonathan Hayase, Creston Brooks, Peiyao Sheng, Himanshu Tyagi, Pramod Viswanath, and Sewoong Oh. 2025. Scalable fingerprinting of large language models. arXiv preprint arXiv:2502.07760

2025
[20]

Mark Russinovich and Ahmed Salem. 2024. Hey, that's my model! introducing chain & hash, an llm fingerprinting technique. arXiv preprint arXiv:2407.10887

2024 arXiv
[21]

ShareGPT. 2023. Sharegpt: Share your wildest chatgpt conversations with one click. https://sharegpt.com/. Accessed on 10/04/2023

2023
[22]

Chenmien Tan, Ge Zhang, and Jie Fu. 2023. Massive editing for large language models via meta learning. arXiv preprint arXiv:2311.04661

2023 arXiv
[23]

Hashimoto

Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca

2023
[24]

Qwen Team. 2024. https://qwenlm.github.io/blog/qwen2.5/ Qwen2.5: A party of foundation models

2024
[25]

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288

2023 arXiv
[26]

Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. 2022. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasin...

2022
[27]

Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al. 2025. The rise and potential of large language model based agents: A survey. Science China Information Sciences, 68(2):121101

2025
[28]

Jiashu Xu, Fei Wang, Mingyu Derek Ma, Pang Wei Koh, Chaowei Xiao, and Muhao Chen. 2024 a . Instructional fingerprinting of large language models. arXiv preprint arXiv:2401.12255

2024 arXiv
[29]

Zhenhua Xu, Meng Han, Xubin Yue, and Wenpeng Xing. 2025 a . https://doi.org/10.1360/SSI-2025-0022 Insty: a robust multi-level cross-granularity fingerprint embedding algorithm for multi-turn dialogue in large language models . SCIENTIA SINICA Informationis, 55(8):1906--1919

2025 doi
[30]

Zhenhua Xu, Zhebo Wang, Maike Li, Wenpeng Xing, Chunqiang Hu, Chen Zhi, and Meng Han. 2025 b . https://arxiv.org/abs/2505.06304 Rap-sm: Robust adversarial prompt via shadow models for copyright verification of large language models . Preprint, arXiv:2505.06304

2025 arXiv
[31]

Zhenhua Xu, Wenpeng Xing, Zhebo Wang, Chang Hu, Chen Jie, and Meng Han. 2024 b . https://arxiv.org/abs/2409.08846 Fp-vec: Fingerprinting large language models via efficient vector addition . Preprint, arXiv:2409.08846

2024 arXiv
[32]

Zhenhua Xu, Xubin Yue, Zhebo Wang, Qichen Liu, Xixiang Zhao, Jingxuan Zhang, Wenjun Zeng, Wengpeng Xing, Dezhang Kong, Changting Lin, and Meng Han. 2025 c . https://arxiv.org/abs/2508.11548 Copyright protection for large language models: A survey of methods, challenges, and tr...

2025 arXiv
[33]

Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023. Editing large language models: Problems, methods, and opportunities. arXiv preprint arXiv:2305.13172

2023 arXiv
[34]

Boyi Zeng, Lizheng Wang, Yuncong Hu, Yi Xu, Chenghu Zhou, Xinbing Wang, Yu Yu, and Zhouhan Lin. 2023. Huref: Human-readable fingerprint for large language models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems

2023
[35]

Jingxuan Zhang, Zhenhua Xu, Rui Hu, Wenpeng Xing, Xuhong Zhang, and Meng Han. 2025. https://doi.org/10.18653/v1/2025.acl-long.1455 ME raser: An effective fingerprint erasure approach for large language models . In Proceedings of the 63rd Annual Meeting of the Association for C...

2025 doi
[36]

Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, et al. 2024. A comprehensive study of knowledge editing for large language models. arXiv preprint arXiv:2401.01286

2024 arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

AI@Meta. 2024. https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md Llama 3 model card

2024

[4] [4]

Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023. Free dolly: Introducing the world’s first truly open instruction-tuned llm. Company Blog of Databricks

work page 2023

[5] [5]

Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv e-prints, pages arXiv--2407

2024

[6] [6]

Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Xiang Wang, Xiangnan He, and Tat-seng Chua. 2024. Alphaedit: Null-space constrained knowledge editing for language models. arXiv preprint arXiv:2410.02355

arXiv 2024

[7] [7]

Jakub Hoscilowicz, Pawel Popiolek, Jan Rudkowski, Jedrzej Bieniasz, and Artur Janicki. 2024. Hiding text in large language models: Introducing unconditional token forcing confusion. arXiv preprint arXiv:2406.02481

work page Pith review arXiv 2024

[8] [8]

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for nlp. In International conference on machine learning, pages 2790--2799. PMLR

2019