REVIEW 5 cited by
REEF: Representation Encoding Fingerprints for Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefore, model owners and third parties need to identify whether a suspect model is a subsequent development of the victim model. To this end, we propose a training-free REEF to identify the relationship between the suspect and victim models from the perspective of LLMs' feature representations. Specifically, REEF computes and compares the centered kernel alignment similarity between the representations of a suspect model and a victim model on the same samples. This training-free REEF does not impair the model's general capabilities and is robust to sequential fine-tuning, pruning, model merging, and permutations. In this way, REEF provides a simple and effective way for third parties and models' owners to protect LLMs' intellectual property together. The code is available at https://github.com/tmylla/REEF.
Forward citations
Cited by 5 Pith papers
-
modelDNA: Calibrated Lineage Verification and Merge Decomposition from Sampled Weight Fingerprints
Sampled weight fingerprints recover LLM parentage with AUROC 1.0 and zero false positives, and recover published mergekit mixture weights without full downloads.
-
CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor
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.
-
FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing
FPEdit uses knowledge editing with a promote-suppress objective to embed robust, stealthy natural-language fingerprints into LLMs, achieving 94 to 100 percent retention after fine-tuning while preserving benchmark per...
-
MEraser: An Effective Fingerprint Erasure Approach for Large Language Models
By fine-tuning on mismatched pairs and then clean pairs, MEraser drops fingerprint success rate to zero on three backdoor-based fingerprinting schemes across multiple LLMs, with a reusable LoRA adapter for transfer.
-
Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models
Backdoor fingerprints trained into LoRA adapters on a base LLM transfer to derivative models with 100% trigger success and, in several scenarios, greater robustness than directly injected fingerprints.
Discussion (0). Sign in to comment.