1 citations · 2 across the 24 of their papers we have counts for
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When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content
Lydia Nishimwe, Benoît Sagot, Rachel Bawden
User-generated content (UGC) is characterised by frequent use of non-standard language, from spelling errors to expressive choices such as slang, character repetitions, and emojis.…
Gaperon: A Peppered English-French Generative Language Model Suite
Nathan Godey, Wissam Antoun, Rian Touchent +4
We release Gaperon, a fully open suite of French-English-coding language models designed to advance transparency and reproducibility in large-scale model training. The Gaperon fami…
LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens
Armel Zebaze, Rachel Bawden, Benoît Sagot
Large reasoning models (LRMs) have led to new possibilities in terms of problem-solving, through the devising of a natural language thought process prior to answering a query. Whil…
TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation
Armel Zebaze, Benoît Sagot, Rachel Bawden
LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning (ICL), rivaling supervised models when translating into high-resource languages…
A French Version of the OLDI Seed Corpus
Malik Marmonier, Benoît Sagot, Rachel Bawden
We present the first French partition of the OLDI Seed Corpus, our submission to the WMT 2025 Open Language Data Initiative (OLDI) shared task. We detail its creation process, whic…
ModernBERT or DeBERTaV3? Examining Architecture and Data Influence on Transformer Encoder Models Performance
Wissam Antoun, Benoît Sagot, Djamé Seddah
Pretrained transformer-encoder models like DeBERTaV3 and ModernBERT introduce architectural advancements aimed at improving efficiency and performance. Although the authors of Mode…
Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation
Armel Zebaze, Benoît Sagot, Rachel Bawden
The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural…
Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression
Nathan Godey, Alessio Devoto, Yu Zhao +4
Autoregressive language models rely on a Key-Value (KV) Cache, which avoids re-computing past hidden states during generation, making it faster. As model sizes and context lengths…
Explicit Learning and the LLM in Machine Translation
Malik Marmonier, Rachel Bawden, Benoît Sagot
This study explores an LLM's ability to learn new languages using explanations found in a grammar book, a process we term "explicit learning." To rigorously assess this ability, we…