arXiv Computation and Language

LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility

arXiv AI

Jul 28

LU-500: A Logo Benchmark for Concept Unlearning

arXiv:2607. 24101v1 Announce Type: cross Abstract: Concept unlearning is increasingly used to limit the reproduction of protected or unsafe visual concepts in text-to-image models.

By Keyu Li, Jin Gao, Jialing Zhang, Dequan Wang

agents diffusion fine-tuning benchmarks

Hugging Face Trending Papers

Jul 27

LU-500: A Logo Benchmark for Concept Unlearning

Concept unlearning is increasingly used to limit the reproduction of protected or unsafe visual concepts in text-to-image models. Existing evaluations, however, mostly study targets that dominate the whole image, such as styles, broad object categories, or portrait-like identities, leaving company logos comparatively underexamined.

agents diffusion fine-tuning benchmarks

arXiv AI

Jul 8

TILDE: TILt-based Distributional Erasure for Concept Unlearning

arXiv:2607. 06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training.

By Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji

diffusion safety

arXiv AI

Sep 15

RA-CoA: Training-free Fashion Image Captioning via Retrieval-Augmented Chain-of-Attributes

arXiv:2609.14100v1 Announce Type: cross Abstract: Fashion Image Captioning (FIC) plays a vital role in enhancing user experience and product search in e-commerce platforms. Unlike natural scene image...

By Abhirama Subramanyam Penamakuri, Shreya Shukla, Anand Mishra

llms rag fine-tuning multimodal

arXiv AI

Aug 25

Deep Contrastive Unlearning for Language Models

Deep Contrastive Unlearning for Language Models (DeepCUT) is a framework that removes information from fine‑tuned language models by directly optimizing their latent space. It addresses the challenge of machine unlearning in black‑box models, which has been largely overlooked by previous work that only mitigated output effects. Experiments on real‑world datasets show that DeepCUT consistently outperforms baseline methods in both effectiveness and efficiency.

By Estrid He, Tabinda Sarwar, Ibrahim Khalil, Xun Yi, Ke Wang

llms diffusion fine-tuning safety

arXiv Computation and Language

1d ago

POET: Preference Optimization for Enhanced Text-to-Image Generation

arXiv:2510.12041v3 Announce Type: replace Abstract: Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecifie...

By Ruibo Chen, Jiacheng Pan, Heng Huang, Zhenheng Yang

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 17

T5-CSBoost: Adversarial Perturbation Resistant LLM Fingerprinting

arXiv:2607. 14113v1 Announce Type: cross Abstract: While many AI-generated text (AIGT) detectors achieve strong performance on clean inputs, their accuracy degrades significantly under light paraphrasing, word substitutions, character edits, and distribution shifts.

By Gayan K. Kulatilleke, Mahsa Baktashmotlagh, Siamak Layeghy, Marius Portmann

llms rag benchmarks safety

arXiv AI

Aug 12

Protecting Creative Writing Copyright against AI Imitation via Implicit Watermarking

arXiv:2504. 00035v4 Announce Type: replace-cross Abstract: Large language models (LLMs) enable powerful knowledge injection through approaches such as in-context learning and fine-tuning, but they also introduce new risks of unauthorized imitation of high-value creative works.

By Ziwei Zhang, Juan Wen, Wanli Peng, Zhengxian Wu, Yinghan Zhou, Yiming Xue

llms nlp fine-tuning

arXiv AI

Jun 18

ProductConsistency: Improving Product Identity Preservation in Instruction-Based Image Editing via SFT and RL

arXiv:2606. 19103v1 Announce Type: cross Abstract: Recent advances in instruction-based image editing have enabled models to perform complex visual edits from natural language instructions.

By Mukund Khanna, Raj Singh Yadav, Kunal Singh

computer-vision reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 10

Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion

arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.

By Rafael Rivera Soto, Barry Chen, Nicholas Andrews

llms benchmarks safety

arXiv AI

Jun 9

ZIPP:Zero-shot Image Personalization from Personas

arXiv:2606. 08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste.

By Harini SI, Somesh Singh, Yaman Kumar Singla, David Doermann, Rajiv Ratn Shah

llms diffusion fine-tuning benchmarks safety

Hugging Face Trending Papers

Aug 4

Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation

Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems.

llms multimodal benchmarks

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