arXiv Machine Learning

Same Text, Different Prediction: Serving-Context Nondeterminism in Text Classifiers

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

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.

By Lucio La Cava, Andrea Tagarelli

llms benchmarks safety

arXiv Machine Learning

Aug 18

Discrete Diffusion Language Models Are Training-Free Multi-Label Classifiers

arXiv:2608. 14649v1 Announce Type: new Abstract: We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification.

By Pawan Kumar

llms diffusion nlp fine-tuning

arXiv Machine Learning

6d ago

Multi-Label Topic Assignment via LLM Distillation: A Comparative Analysis of Generative vs. Discriminative Student Models

arXiv:2610.09063v1 Announce Type: new Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability c...

By Sourabh Kasliwal, Shubhranshu Singh

llms efficiency

arXiv Machine Learning

Aug 20

Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text

The paper introduces Pattern Stability Score (PSS), a watermark detection framework that uses local statistical features and stability dynamics across paraphrased variants to identify machine-generated text. PSS combines global and local z‑score features with higher‑order run‑length statistics, autocorrelation signals, and stability scores over paraphrase depth. Experiments on PG‑19, CNN/DailyMail, and WikiText with Llama‑3‑8B, Qwen2‑7B, and multiple paraphrasers show that PSS improves detection AUC by 10‑15 percentage points and a single universal classifier achieves over 87.8% AUC across diverse LLMs, paraphrasers, and domains without retraining.

By Sina Mansouri, Mohit Marvania, Abolfazl Safikhani

llms benchmarks

arXiv Machine Learning

Aug 11

UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers

arXiv:2608. 09209v1 Announce Type: cross Abstract: Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs.

By Chidaksh Ravuru, Shashank Srivastava

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 21

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.

By Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu, Robin Jia

nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 5

When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer

arXiv:2606. 05626v1 Announce Type: cross Abstract: Machine-generated text (MGT) attribution aims to identify the specific generator responsible for a given text, thereby providing fine-grained evidence for model accountability and misuse investigation.

By Zhen Sun, Yifan Liao, Zhicong Huang, Jiaheng Wei, Cheng Hong, Yutao Yue, Xinlei He

llms

arXiv Machine Learning

Sep 22

Logits are All We Need to Adapt Closed Models

arXiv:2502.06806v5 Announce Type: replace Abstract: Many commercial Large Language Models (LLMs) are often closed-source, limiting developers to prompt tuning for aligning content generation with spe...

By Gaurush Hiranandani, Haolun Wu, Subhojyoti Mukherjee, Sanmi Koyejo

llms

arXiv AI

Aug 3

TextCloak: Thwarting Unauthorized LLM Exploitation via RL-Driven Unlearnable Text

arXiv:2607. 28862v1 Announce Type: cross Abstract: The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about unauthorized data exploitation and privacy leakage.

By Chengshuai Zhao, Pingchuan Ma, Dawei Li, Bohan Jiang, Zhiyuan Yu, Zhen Tan, Huan Liu

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Oct 2

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...

By Luxu Liang, Xiang Li

llms safety

arXiv AI

Oct 2

Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs

arXiv:2610.01428v1 Announce Type: cross Abstract: Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed...

By Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein, Rom Himelstein, Avi Mendelson, Amit LeVi

llms benchmarks

Related stories