RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text (2020.emnlp-demos)
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| Challenge: | Existing studies on how humans perceive machine-generated text are limited due to the prohibitive cost of running human evaluation studies. |
| Approach: | They propose a task to detect the boundary at which a text passage starts off human-written transitions to being machine-generated. |
| Outcome: | The proposed system evaluates machine-generated news articles on a wide range of domains. |
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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. |
| Approach: | This tutorial aims to provide a comprehensive overview of text detection techniques . it will focus on machine-generated text and deepfakes . |
| Outcome: | This tutorial focuses on machine-generated text and deepfakes. |
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021.acl-long)
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| Challenge: | evaluators distinguish between human- and machine-authored text in three domains without training . evals' accuracy improved up to 55%, but it did not significantly improve across the three domain. |
| Approach: | They examine the role untrained human evaluations play in NLG evaluation and propose ways to improve their evaluations. |
| Outcome: | The evaluators distinguished between human- and machine-authored text at random chance level without training, but their accuracy did not improve across the three domains. |
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. |
Automatic Detection of Machine Generated Text: A Critical Survey (2020.coling-main)
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| Challenge: | Current text generative models excel in producing text that matches the style of human language reasonably well. |
| Approach: | They conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area. |
| Outcome: | The proposed detectors can distinguish between human and text generated by the model and can be used to generate fake news and fake product reviews. |
Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)
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| 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. |
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack (2024.lrec-main)
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| Challenge: | Despite the development of large language models, there are still significant challenges in detecting whether text is generated by a machine. |
| Approach: | They propose a framework for a broader class of adversarial attacks to perform minor perturbations in machine-generated content to evade detection. |
| Outcome: | The proposed framework can be compromised in as little as 10 seconds, and improves over iterative adversarial learning. |
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. |
| Approach: | They compare decoding methods with popular sampling-based decoding strategies . they show that multi-sentence excerpts can fool expert human raters over 30% of the time . |
| Outcome: | The proposed methods improve with longer excerpt length, but multi-sentence excerpts fool human raters over 30% of the time. |
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
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| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
| Approach: | They propose a benchmark environment to test the 'Turing Test' problem for neural text generation methods. |
| Outcome: | The proposed benchmark environment is based on 200K human- or machine-generated samples across 20 labels Human, GPT-1, GTP-2_small, GTT-2_medium, GPG-2_large, GGT-2_PyTorch, GGP-3, GROVER_base, griover_large and GRover_mega. |
A Practical Examination of AI-Generated Text Detectors for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing methods to detect large language models are prone to misuse, such as generating fake news articles, facilitating academic plagiarism or spamming. |
| Approach: | They evaluate several popular detectors to evaluate their effectiveness against a range of domains, datasets, and models. |
| Outcome: | The proposed methods perform poorly in certain settings, with TPR@.01 as low as 0%. |
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection (2024.eacl-long)
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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate fluent responses to user queries, but they are also susceptible to misuse in journalism, education, and academia. |
| Approach: | They propose a large-scale benchmark for machine-generated text detection that is a multi-generator, multi-domain, and multi-lingual corpus. |
| Outcome: | The proposed system can detect machine-generated text and pinpoint misuse . the proposed system is based on a large-scale benchmark dataset . |