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.
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How close is ChatGPT to human experts? Comparison corpus, evaluation, and detection
16 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
citing papers explorer
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Segmenting Human-LLM Co-authored Text via Change Point Detection
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.
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ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability
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.
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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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More Aligned, Less Diverse? Analyzing the Grammar and Lexicon of Two Generations of LLMs
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.
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LLM Output Detectability and Task Performance Can be Jointly Optimized
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.
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Rethinking Publication: A Certification Framework for AI-Enabled Research
A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
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All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding
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.
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Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection
RACE applies rhetorical structure analysis to model creator and editor roles separately for four-class fine-grained detection of LLM-generated text.
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GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization
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.
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Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement
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.
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Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection
A multi-level framework that models local and global relations among token detection scores to improve machine-generated text detection with low overhead.
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Feature-Augmented Transformers for Robust AI-Text Detection Across Domains and Generators
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.
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Please Make it Sound like Human: Encoder-Decoder vs. Decoder-Only Transformers for AI-to-Human Text Style Transfer
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.
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LifeAlign: Lifelong Alignment for Large Language Models with Memory-Augmented Focalized Preference Optimization
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.
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Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
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.
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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.