arXiv Machine Learning
Jul 2
AdaBoosting Text Prompts for Vision-Language Models
arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.
Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration
arXiv:2608. 05741v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational misuse, and platform governance.
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arXiv AI
Jul 7
Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability
arXiv:2607. 04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge.
arXiv Machine Learning
Aug 3
PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction
arXiv:2607. 29378v1 Announce Type: cross Abstract: Large language models (LLMs) generate text by auto-regressively sampling the next token.
arXiv Machine Learning
Jun 9
Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search
arXiv:2512. 08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age.
arXiv Machine Learning
Jun 24
Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation
arXiv:2507. 09839v2 Announce Type: replace Abstract: An increasing number of NLP applications interact with large language models (LLMs) through black-box APIs, making prompt engineering critical for controlling model behavior.
arXiv Machine Learning
Jun 15
Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models
arXiv:2512. 04981v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) systems increasingly rely on Large Language Model (LLM)-based text conditioning to interpret and expand user prompts.
arXiv AI
Jun 4
Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling
arXiv:2604. 25860v2 Announce Type: replace-cross Abstract: Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specific fingerprints.
arXiv AI
Aug 5
Test-Time Scaling for Safe Text-Guided Image Generation via Intermediate Clean Estimates
arXiv:2608. 03284v1 Announce Type: cross Abstract: Ensuring safety and policy compliance in text-to-image diffusion models remains a critical challenge, as benign or adversarial prompts can often elicit prohibited content, e.
arXiv AI
Jun 6
LLM Self-Recognition: Steering and Retrieving Activation Signatures
arXiv:2606. 06315v1 Announce Type: new Abstract: Recent advances in interpretability suggest that large language models (LLMs) implicitly encode signals in their generated text that enable self-recognition of their outputs.
arXiv AI
Aug 5
Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes
arXiv:2608. 02879v1 Announce Type: new Abstract: The widespread adoption of proprietary Large Language Models (LLMs) accessed strictly through closed APIs has created a critical challenge for responsible deployment: a fundamental lack of interpretability.
arXiv AI
Jun 9
MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models
arXiv:2606. 07996v1 Announce Type: cross Abstract: Pretraining is fundamental to the development of Large Language Models (LLMs), yet the opacity of pretraining data complicates model analysis and raises ethical, legal, and fairness concerns.
arXiv Machine Learning
Jun 3
BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks
arXiv:2606. 02947v1 Announce Type: new Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks.