Papers with text detection methods
Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions (2021.findings-acl)
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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. |
| Approach: | They propose to use linguistic alignment to detect bot-generated text rather than using it directly. |
| Outcome: | The proposed methods are more robust across datasets and models if they use information about how people respond to it rather than using the bot's text directly. |
How to Generalize the Detection of AI-Generated Text: Confounding Neurons (2025.findings-emnlp)
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| Challenge: | Linguistic and domain confounders introduce spurious correlations, leading to poor out-of-distribution (OOD) performance. |
| Approach: | They propose a novel post-hoc, neuron-level intervention framework to disentangle AI-generated text detection factors from data-specific biases. |
| Outcome: | The proposed framework reduces topic-specific biases by encoding individual neurons within transformers-based detectors rather than task-specific signals. |