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 Machine Learning
Sep 14
Limits of LLM Text Detectors in Education
The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.
arXiv Machine Learning
2d ago
Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts
arXiv:2605.12890v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-writte...
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 AI
4d ago
How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs
The paper introduces ramework, a blackbox prompt‑minimization framework that identifies the minimal subset of few‑shot prompts necessary for large language models (LLMs). In a case study, the framework reduces few‑shot exemplars by an average of 65.3% in character count while maintaining full propositional output fidelity, revealing that models tend to keep logical identifiers and constraint declarations while discarding natural language prose. The analysis further distinguishes between universal encoder and decoder models, offering insights into prompt compression and structural analysis.
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.
Hugging Face Trending Papers
Aug 25
Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out-of-domain (OOD) a...
arXiv AI
2d ago
UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models
UniGuardian is a training‑free detector for large language models that jointly identifies prompt injection, backdoor, and adversarial attacks—collectively called Prompt Trigger Attacks (PTA). It measures how structured prompt perturbations shift the model’s output distribution and uses a single‑forward strategy to detect attacks while generating text in a shared batched forward pass. Experiments show that UniGuardian accurately and efficiently identifies trigger‑activated prompts in LLMs.
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.