Papers by Hoang-Quoc Nguyen-Son
Machine Translated Text Detection Through Text Similarity with Round-Trip Translation (2021.naacl-main)
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| Challenge: | Existing detectors for translating texts fail to detect a text from a strange translator . Existing methods for detection of translated texts use text structure and complex words to detect translations . |
| Approach: | They propose a detector using text similarity with round-trip translation (TSRT) TSRT achieves 86.9% accuracy in detecting a translated text from a strange translator . Existing detectors have been built around a specific translator but fail to detect a translation from skeptics . |
| Outcome: | Existing detectors fail to detect translated texts from a strange translator . a detector achieves 86.9% accuracy in detecting a translated text from skeptic translators . |
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations (2023.findings-acl)
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| Challenge: | Existing methods for detecting adversarial attacks need original/adversarial data. |
| Approach: | They propose to detect adversarial text by voting on hard labels from predictions of transformations using a model that uses original/adversarial data. |
| Outcome: | The proposed method detects adversarial text across various state-of-the-art attacks, models, and datasets without training. |
CheckHARD: Checking Hard Labels for Adversarial Text Detection, Prediction Correction, and Perturbed Word Suggestion (2022.findings-emnlp)
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| Challenge: | Existing work detects adversarial text and corrects the prediction by identifying perturbed words and changing them into their synonyms, but many benign words are also changed. |
| Approach: | They propose to detect adversarial text, correct the prediction, and suggest perturbed words by checking the change in the hard labels from the target’s predictions. |
| Outcome: | The proposed model outperforms existing work on various attacks, models, and datasets. |
SimLLM: Detecting Sentences Generated by Large Language Models Using Similarity between the Generation and its Re-generation (2024.emnlp-main)
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| Challenge: | Prior studies have detected the generation of non-analogous text with substantial differences between original and generated content. |
| Approach: | They propose a method to detect analogous machine-generated sentences that closely mimic human-written ones by estimating the similarity between an input sentence and its generated counterpart. |
| Outcome: | The proposed approach outperforms existing methods in academic dishonesty, spam dissemination, and misinformation propagation. |
SearchLLM: Detecting LLM Paraphrased Text by Measuring the Similarity with Regeneration of the Candidate Source via Search Engine (2026.eacl-long)
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| Challenge: | Large language models (LLMs) can be used to enhance text quality but can sometimes result in loss or distortion of original meaning. |
| Approach: | They propose a method to identify LLM-paraphrased text by leveraging search engine capabilities to locate potential original text sources. |
| Outcome: | The proposed approach distinguishes LLM-paraphrased text from genuine human writing . it uses search engine capabilities to integrate with existing detectors to improve performance . |