Image de couverture de livre

vTune: Verifiable Fine-Tuning for LLMs Through Backdooring

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhang, Eva, Pal, Arka, Potti, Akilesh, Goldblum, Micah
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence Computers and Society
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs
par: Pal, Arka, et autres
Publié: (2026)

Knowing What You Know Is Not Enough: Large Language Model Confidences Don't Align With Their Actions
par: Pal, Arka, et autres
Publié: (2025)

An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs
par: Thomas, Rahul, et autres
Publié: (2025)

Cascade: Token-Sharded Private LLM Inference
par: Thomas, Rahul, et autres
Publié: (2025)

Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs
par: Hamman, Faisal, et autres
Publié: (2024)

SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs
par: Zhao, Yanxiao, et autres
Publié: (2025)

Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling
par: Liu, Yuanhao, et autres
Publié: (2026)

Tuning Derivatives for Causal Fairness in Machine Learning
par: Edström, Filip, et autres
Publié: (2026)

Backdoor for Debias: Mitigating Model Bias with Backdoor Attack-based Artificial Bias
par: Wu, Shangxi, et autres
Publié: (2023)

FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
par: Li, Zhonghang, et autres
Publié: (2024)

Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias
par: Itzhak, Itay, et autres
Publié: (2023)

EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs
par: Guo, Song, et autres
Publié: (2024)

Large Language Models Must Be Taught to Know What They Don't Know
par: Kapoor, Sanyam, et autres
Publié: (2024)

Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach
par: Chakrabarty, Sayak, et autres
Publié: (2024)

Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
par: Ovadia, Oded, et autres
Publié: (2023)

Teaching LLMs How to Learn with Contextual Fine-Tuning
par: Choi, Younwoo, et autres
Publié: (2025)

Understanding and Preserving Safety in Fine-Tuned LLMs
par: Zhang, Jiawen, et autres
Publié: (2026)

Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
par: Arteaga, Gabriel Y., et autres
Publié: (2024)

Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis
par: Wang, Xu, et autres
Publié: (2025)

Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
par: Rallapalli, Swati, et autres
Publié: (2025)

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning
par: Khadangi, Afshin, et autres
Publié: (2025)

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
par: Hans, Abhimanyu, et autres
Publié: (2024)

Parameter-Efficient Fine-Tuning of LLMs with Mixture of Space Experts
par: Zhang, Buze, et autres
Publié: (2026)

Order-Independence Without Fine Tuning
par: McIlroy-Young, Reid, et autres
Publié: (2024)

Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs
par: Zhang, Zheng, et autres
Publié: (2024)

Long Exposure: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity
par: Wang, Tuowei, et autres
Publié: (2025)

Thompson Sampling via Fine-Tuning of LLMs
par: Menet, Nicolas, et autres
Publié: (2025)

Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
par: Kang, Feiyang, et autres
Publié: (2024)

Proximal Supervised Fine-Tuning
par: Zhu, Wenhong, et autres
Publié: (2025)

LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
par: Kim, Taeho, et autres
Publié: (2024)

ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine
par: Qasem, Rabee, et autres
Publié: (2024)

Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization
par: Thulke, David, et autres
Publié: (2024)

Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs
par: Goel, Raghavv, et autres
Publié: (2024)

Fine-Tuning is Fine, if Calibrated
par: Mai, Zheda, et autres
Publié: (2024)

Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
par: Xu, Jiashu, et autres
Publié: (2023)

Parameter-Efficient Fine-Tuning for Foundation Models
par: Zhang, Dan, et autres
Publié: (2025)

The Lie Derivative for Measuring Learned Equivariance
par: Gruver, Nate, et autres
Publié: (2022)

Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs
par: Wang, Ruoyu, et autres
Publié: (2024)

Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
par: Ling, Zhenqing, et autres
Publié: (2025)

Re-Emergent Misalignment: How Narrow Fine-Tuning Erodes Safety Alignment in LLMs
par: Giordani, Jeremiah
Publié: (2025)