Papers by Wissam Antoun
Gaperon: A Peppered English-French Generative Language Model Suite (2026.findings-acl)
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Nathan Godey, Wissam Antoun, Rian Touchent, Rachel Bawden, Éric Villemonte de la Clergerie, Benoît Sagot, Djamé Seddah
| Challenge: | Standardized benchmarks have become the dominant metric for measuring progress in large language models, but their validity is compromised by data contamination and unclear relationship between benchmark scores and genuine language understanding. |
| Approach: | They propose to use GAPERON to investigate evaluation dynamics under realistic training conditions. |
| Outcome: | The proposed model outperforms models that excel on benchmarks in qualitative text generation and vice versa. |
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. |
| Outcome: | The proposed method detects text from target LLMs without further training. |
Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection (2025.coling-main)
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| Challenge: | Existing datasets and models fail to address the complexities of multilingual data, authors say . detection of radical content on online platforms has become an increasingly pressing concern . |
| Approach: | They propose a publicly available multilingual dataset annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
| Outcome: | The proposed dataset is annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
Data-Efficient French Language Modeling with CamemBERTa (2023.findings-acl)
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| Challenge: | Recent advances in NLP have significantly improved the performance of language models on a variety of tasks. |
| Approach: | They introduce a French DeBERTa model that builds upon the DeBERTAV3 architecture and training objective and evaluate its performance on a variety of French downstream tasks and datasets. |
| Outcome: | The proposed model outperforms BERT-based models on most tasks given the same amount of training tokens and trained on 30% of its input tokens. |