Papers by Arnaud Delhay
SocialForge: simulating the social internet to provide realistic training against influence operations (2025.acl-industry)
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Ulysse Oliveri, Guillaume Gadek, Alexandre Dey, Benjamin Costé, Damien Lolive, Arnaud Delhay, Bruno Grilheres
| Challenge: | Social media platforms have enabled large-scale influence campaigns, impacting democratic processes. |
| Approach: | They propose a system to enhance diversity and realism of the generated content while ensuring its adherence to the original scenario. |
| Outcome: | The proposed system improves diversity and realism while ensuring its adherence to the original scenario. |
Paraphrase Generation Evaluation Powered by an LLM: A Semantic Metric, Not a Lexical One (2025.coling-main)
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| Challenge: | Existing measures for automatic paraphrase generation are based on lexical distances or semantic embedding alignments. |
| Approach: | They propose a measure based on a log likelihood ratio from an LLM to assess the quality of a potential paraphrase. |
| Outcome: | The proposed measure is better for sorting pairs of sentences by semantic proximity and provides an interpretable classification threshold between paraphrases and non-paraphrases. |
EMO&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context. (L18-1)
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| Challenge: | Anomalies in discourse are induced or acted by a machine learning algorithm. |
| Approach: | They propose to use facial and speech video to create a corpus that contains controlled anomalies. |
| Outcome: | The proposed corpus contains controlled anomalies in speech and facial video recordings of subjects. |
When depth is redundant: Efficient transformer-based speech anti-spoofing (2026.findings-acl)
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| Challenge: | Existing anti-spoofing countermeasures exhibit limited generalization to unseen spoof attacks, especially in out-of-domain evaluation settings. |
| Approach: | They propose a training strategy that aligns shallow and intermediate representations with those of the final transformer layer for speech deepfake detection. |
| Outcome: | The proposed model improves robustness to unseen spoofing attacks and enhances out-of-domain generalization over strong baselines. |