DEPO formulates detector-evasive paraphrasing as a constrained MDP and solves it via Lagrangian primal-dual RL with GRPO-style updates to achieve evasion while satisfying a semantic-preservation constraint.
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How close is chatgpt to human experts? comparison corpus, evaluation, and detection
21 Pith papers cite this work. Polarity classification is still indexing.
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Adapts change point detection to segment human-LLM co-authored text using weighted and generalized algorithms with minimax optimality and strong empirical results against baselines.
ExaGPT uses span-level similarity retrieval from human and LLM datastores to detect machine-generated text while supplying the matching spans as human-interpretable evidence, achieving up to 37-point accuracy gains over prior interpretable detectors at 1% FPR.
Triospect combines statistical, content, and expression views to detect AI text more robustly, reporting AUROC gains of 22.3% and 9.1% on two attacked benchmarks across 17 attacks and 17 models.
Steering LLM residual streams with random sparse vectors creates detectable self-recognition fingerprints that enable over 98% accurate attribution of generated text to specific models without degrading output quality.
DetectZoo is a unified toolkit providing reference implementations of 61 detectors, native loaders for 22 benchmark datasets, and a standardized evaluation pipeline for AI-generated content detection across text, audio, and image modalities.
MELD is a multi-task AI-text detector using auxiliary heads, uncertainty-weighted losses, EMA distillation, and pairwise ranking that reaches 99.9% TPR at 1% FPR on a new held-out benchmark while remaining competitive on the RAID leaderboard.
Newer LLMs exhibit reduced syntactic and lexical diversity in English news text generation compared to older models, as measured by HPSG grammar and diversity metrics from ecology and information theory, while human-authored text shows little change.
PUPPET jointly optimizes LLM outputs for high detectability and task performance via RL rewards from a detector and a task evaluator, outperforming watermarking on tasks while matching detectability.
A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
A unified synthetic data generation pipeline produces unlimited annotated multimodal video data across multiple tasks, enabling models trained mostly on synthetic data to generalize effectively to real-world video understanding benchmarks.
RACE applies rhetorical structure analysis to model creator and editor roles separately for four-class fine-grained detection of LLM-generated text.
GigaCheck detects LLM-generated text at both document and span levels by combining fine-tuned language-model embeddings with a DETR-like architecture that treats generated intervals as detectable objects.
Reveals hidden human-like spans in machine-generated texts that raise detection complexity and proposes a stacked enhancement framework with hard-EM optimization to improve detectors across LLMs.
A multi-level framework that models local and global relations among token detection scores to improve machine-generated text detection with low overhead.
Feature-augmented DeBERTa-v3-base with attention-based fusion reaches 85.9% balanced accuracy on the multi-domain M4 benchmark under fixed-threshold evaluation, outperforming zero-shot baselines by up to 7.22 points.
BART-large outperforms Mistral-7B in AI-to-human style transfer with higher reference similarity scores and far fewer parameters, while showing that marker shift can reflect overshoot rather than accurate transfer.
LifeAlign uses focalized preference optimization and short-to-long memory consolidation via dimensionality reduction to let LLMs align with new preferences while retaining prior knowledge.
Humans detect AI-generated text at 87.6% accuracy across 9 languages and 9 domains, outperforming prior near-random results, and do not always prefer human-written text when the source is unclear.
This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.
Fine-tuned multilingual LLMs achieve top shared-task scores on financial causality extraction in English and Spanish.
citing papers explorer
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MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text
MELD is a multi-task AI-text detector using auxiliary heads, uncertainty-weighted losses, EMA distillation, and pairwise ranking that reaches 99.9% TPR at 1% FPR on a new held-out benchmark while remaining competitive on the RAID leaderboard.
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A Survey of Large Language Models
This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.