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Adaptation au domaine de modèles de langue à l’aide de réseaux à base de graphes (Graph Neural Networks for Adapting General Domain Language Modèles Specialised Corpora)
De plus, ces modèles négligent souvent les informations globales sur le vocabulaire au profit d’une plus forte dépendance à l’attention.
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data
Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and an important basis for further natural language processing applications.
Graph Neural Networks for Adapting Off-the-shelf General Domain Language Models to Low-Resource Specialised Domains
Language models encode linguistic proprieties and are used as input for more specific models.
Low-Cost Language Models: Survey and Performance Evaluation on Python Code Generation
Large Language Models (LLMs) have become the go-to solution for many Natural Language Processing (NLP) tasks due to their ability to tackle various problems and produce high-quality results.
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