CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |
Similar Papers
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)
Copied to clipboard
Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun
| Challenge: | Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT. |
| Approach: | They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness. |
| Outcome: | The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability. |
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors (2025.findings-acl)
Copied to clipboard
Andrea Pedrotti, Michele Papucci, Cristiano Ciaccio, Alessio Miaschi, Giovanni Puccetti, Felice Dell’Orletta, Andrea Esuli
| Challenge: | Recent advances in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, raising concerns about the potential for malicious use, such as misinformation and manipulation. |
| Approach: | They evaluate the resilience of state-of-the-art MGT detectors to linguistically informed adversarial attacks by using Direct Preference Optimization to shift the MGT style toward human-written text. |
| Outcome: | The proposed pipeline fine-tunes language models to shift the MGT style toward human-written text (HWT) it obtains generations more challenging to detect by current models, and shows that detectors can be easily fooled with relatively few examples, resulting in a significant drop in detecting performances. |
Machine-Generated Text Localization (2024.findings-acl)
Copied to clipboard
| Challenge: | Prior work focused on identifying only part of a document as machine or human written . a key challenge is that short spans of text provide little information indicating if it is machine generated due to its short length . |
| Approach: | They propose a method that localizes the portions of a document that were machine generated. |
| Outcome: | The proposed method can detect changes in style or content to boost performance. |
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection (2024.acl-long)
Copied to clipboard
Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Large Language Models (LLMs) have brought an unprecedented surge in machine-generated text (MGT) societal implications are posed by their potential misuse and lack of training data. |
| Approach: | They propose a benchmark to detect machine-generated text in multiple languages . they use multi-domain and multi-generator corpus to identify which model generated the text . |
| Outcome: | The proposed benchmark compares a multilingual, multi-domain and multi-generator corpus of MGTs with human-generated content. |
HACo-Det: A Study Towards Fine-Grained Machine-Generated Text Detection under Human-AI Coauthoring (2025.acl-long)
Copied to clipboard
| Challenge: | Existing literature focuses on binary, document-level detection, neglecting texts composed jointly by human and LLM contributions. |
| Approach: | They propose to use a dataset to generate human-AI coauthored texts via an automatic pipeline with word-level attribution labels. |
| Outcome: | The proposed method can detect human-AI coauthored texts with a numeric AI ratio. |
TEXT-CAKE: Challenging Language Models on Local Text Coherence (2025.coling-main)
Copied to clipboard
| Challenge: | a new evaluation benchmark, TEXT-CAKE, is used to evaluate language models for text coherence detection. |
| Approach: | They propose a benchmark to evaluate language models' ability to detect text coherence . they analyze multilingual and monolingual LMs with varying architectures and parameters . |
| Outcome: | The proposed model outperforms other models on the TEXT-CAKE evaluation benchmark. |
Authorship Obfuscation in Multilingual Machine-Generated Text Detection (2024.findings-emnlp)
Copied to clipboard
Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | Recent advances in Language Modeling have birthed Large Language Models (LLMs), which exhibit significant improvements, including the ability to generate texts easily misconstrued as humanwritten. |
| Approach: | They compare authorship obfuscation methods against machine-generated text (MGT) in 11 languages and analyze their performance against 37 well-known AO methods. |
| Outcome: | The proposed methods can cause evasion of detection in all languages, with homoglyph attacks particularly successful. |
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training (2025.acl-long)
Copied to clipboard
| Challenge: | Existing MGT detectors are vulnerable to simple perturbations and adversarial attacks. |
| Approach: | They propose an adversarial framework for training a robust machine-generated text detector called GREedy Adversary PromoTed DefendER. |
| Outcome: | The proposed framework reduces the Attack Success Rate (ASR) by 0.67% compared with SOTA defense methods. |
Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)
Copied to clipboard
| Challenge: | Recent advances in the quality of the generation of text by large language models have spurred research into identifying machine-generated text. |
| Approach: | They audit classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. |
| Outcome: | The proposed methods are highly sensitive to stylistic changes and complexity, and in some cases degrade entirely to random classifiers. |
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks (D19-1)
Copied to clipboard
| Challenge: | Existing methods to learn general representations of text can achieve sub-optimal performance in low-resource scenarios. |
| Approach: | They propose to use language model pre-training and multi-task learning to learn robust representations but these methods can achieve sub-optimal performance in low-resource scenarios. |
| Outcome: | The proposed model outperforms strong baselines on the GLUE benchmark and can be adapted to new tasks efficiently and effectively. |