Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)
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| Challenge: | Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints. |
| Approach: | They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task. |
| Outcome: | The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. |
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| Challenge: | Pragmatics and non-literal language understanding are essential to human communication . a long-standing challenge for artificial language models is to capture pragmatics . |
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| Challenge: | Using large language models to detect machine generated text is difficult for humans to distinguish between human-written and machine-generated text. |
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Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI (2026.acl-long)
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Yuxia Wang, Rui Xing, Jonibek Mansurov, Giovanni Puccetti, Zhuohan Xie, Minh Ngoc Ta, Jiahui Geng, Jinyan Su, Mervat Abassy, Saadeldine Eletter, Kareem Elozeiri, Nurkhan Laiyk, Maiya Goloburda, Tarek Mahmoud, Raj Vardhan Tomar, Alexander Aziz, Ryuto Koike, Masahiro Kaneko, Artem Shelmanov, Ekaterina Artemova, Vladislav Mikhailov, Akim Tsvigun, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
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| Challenge: | Language generation models' democratization makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech. |
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From Text to Source: Results in Detecting Large Language Model-Generated Content (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) generate human-like text, but have ethical and misuse concerns. |
| Approach: | They evaluate whether a classifier trained to distinguish between source and target LLMs can detect text from an LLM without further training. |
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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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Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
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| Challenge: | Existing work has shown that non-linguistic biases in language models obscure linguistic knowledge. |
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Automatic Detection of Generated Text is Easiest when Humans are Fooled (2020.acl-main)
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| Challenge: | Recent advances in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. |
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Detecting Label Errors by Using Pre-Trained Language Models (2022.emnlp-main)
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| Challenge: | Existing methods for label error detection focus on label errors in training data. |
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