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cs.CL2026
To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMs
Franck Signe, Hippolyte Pilchen, François Yvon +1
Multilingual Large Language Models (LLMs) traditionally rely on a single vocabulary shared by all supported languages, which can lead to uneven compression across them. Moreover, t…
cs.CL2026
Understanding Data Temporality Impact on Large Language Models Pre-training
Hippolyte Pilchen, Romain Fabre, Franck Signe Talla +2
Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understo…
cs.CL2025
ARC-Encoder: learning compressed text representations for large language models
Hippolyte Pilchen, Edouard Grave, Patrick Pérez
Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs. Context compression techniques can…