| Challenge: | The paper extends the Data Movement Distance (DMD) metric defined to measure the locality in computer memory to text by defining a new term designed to better characterize low-frequency tokens. |
| Approach: | They propose to define a normalized version of the Data Movement Distance (nDMD) term is designed to better characterize low-frequency tokens. |
| Outcome: | The proposed normalized version outperforms baselines and improves performance on the English subset of the M4 dataset and the GenAI detection shared task. |
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| Challenge: | a large study of historical text normalization is done on eight languages . there is no consensus on the state-of-the-art approach to normalization . |
| Approach: | They present a large study of historical text normalization done on eight languages . they evaluate four different systems based on supervised learning on datasets from eight different languages based in the literature . |
| Outcome: | The proposed methods are based on supervised learning and are available online. |
Linguistic and Embedding-Based Profiling of Texts Generated by Humans and Large Language Models (2025.emnlp-main)
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| Challenge: | Recent studies have focused on using LLMs to classify text as either human-written or machine-generated . |
| Approach: | They characterize human-written and machine-generated texts using a set of linguistic features across different linguistic levels such as morphology, syntax, and semantics. |
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EvoBench: Towards Real-world LLM-Generated Text Detection Benchmarking for Evolving Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods to detect LLM-generated texts rely on static benchmarks that neglect the evolving nature of LLMs. |
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PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech (2025.emnlp-industry)
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| Challenge: | Text Normalization (TN) is a key preprocessing step in Text-to-Speech systems. |
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QA Analysis in Medical and Legal Domains: A Survey of Data Augmentation in Low-Resource Settings (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) have revolutionized natural language processing, but their success remains limited to high-resource domains. |
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From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MarkerGen (2025.acl-long)
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Peiwen Yuan, Chuyi Tan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Existing methods to control text length are lacking in LCTG, posing a major limitation for practical applications. |
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Learning to Rewrite: Generalized LLM-Generated Text Detection (2025.acl-long)
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| Challenge: | Existing detectors for Large Language Models (LLMs) struggle to generalize in open-world settings. |
| Approach: | They propose a framework to detect LLM-generated text with exceptional generalization to unseen domains by reinforcing LLMs’ inherent rewriting tendencies. |
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GPT-who: An Information Density-based Machine-Generated Text Detector (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) generate misinformation, memorized content, plagiarized content, toxic speech, and hallucinated content. |
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Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
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HLU: Human Vs LLM Generated Text Detection Dataset for Urdu at Multiple Granularities (2025.coling-main)
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| Challenge: | Using large language models (LLMs) to generate human-like text has raised concerns about misuse, especially in low-resource languages like Urdu. |
| Approach: | They propose a dataset that contains documents, paragraphs, and sentences . they conducted human evaluations and automated evaluations . |
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