CHIFRAUD: A Long-term Web Text Dataset for Chinese Fraud Detection (2025.coling-main)
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| Challenge: | Detecting fraudulent online text is essential as they exploit human greed and deceive individuals. |
| Approach: | They propose to build a long-term dataset of Chinese fraudulent texts collected over 12 months. |
| Outcome: | The proposed dataset includes 59,106 entries extracted from billions of web pages and includes large language model-based detectors and pre-trained language model approaches. |
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| Challenge: | Recent efforts to develop algorithms for large language models (LLMs) have limited model diversity and data homogeneity in the Chinese corpora. |
| Approach: | They propose a Chinese Real-prompt AI-generated text Detection benchmark that can be generalized to unseen LLMs and external Chinese datasets. |
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Beyond Read-Only: Crafting a Comprehensive Chinese Text-to-SQL Dataset for Database Manipulation and Query (2024.findings-naacl)
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| Challenge: | Current research focuses mainly on read operations and ignores other aspects of database operations such as create, update, and delete operations. |
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CHisIEC: An Information Extraction Corpus for Ancient Chinese History (2024.lrec-main)
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| Challenge: | Historical and cultural heritage preservation is an important branch of digital humanities, where the rich tapestry of the past meets the cutting-edge tools of the digital age. |
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Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking (D19-55)
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Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler
| Challenge: | Illicit activity on the Web often obscures information between client and seller, such as the seller’s phone number. |
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WikiHan: A New Comparative Dataset for Chinese Languages (2022.coling-1)
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| Challenge: | Currently, there are 1.3 billion speakers of Sinitic varieties, making the family one of the largest in terms of speaker count. |
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Detecting Machine-Generated Text: Techniques and Challenges (2024.acl-tutorials)
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| Challenge: | This tutorial focuses on machine-generated text and deepfakes. |
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MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)
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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang
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Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study (2023.emnlp-main)
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| Challenge: | a network of doppelganger websites (impersonating genuine news sites) was discovered in 2022 . a novel dataset enables studies of disinformation networks and the training of NLP tools for disinformation detection. |
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SCCD: A Session-based Dataset for Chinese Cyberbullying Detection (2025.coling-main)
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| Challenge: | Existing work on cyberbullying detection in Chinese is underdeveloped due to the lack of comprehensive and reliable datasets. |
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GLTR: Statistical Detection and Visualization of Generated Text (P19-3)
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| Challenge: | GLTR is a tool to detect generated text that can be used by non-experts. |
| Approach: | They propose a tool to detect generated text using a set of statistical methods that can be used by non-experts. |
| Outcome: | The proposed method improves detection rate of fake text from 54% to 72% without training. |