Papers by Yanis Labrak
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored . |
| Approach: | They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English. |
| Outcome: | The evaluated models outperform state-of-the-art models on 13 real-world clinical and biomedical NLP tasks in English. |
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain (2024.lrec-main)
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Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant Dit Beaufils, Natalia Grabar, Béatrice Daille, Solen Quiniou, Emmanuel Morin, Pierre-Antoine Gourraud, Richard Dufour
| Challenge: | Existing benchmarks for pre-trained language models are limited to only a few languages . a limited number of tasks are evaluated on non-standardized protocols . |
| Approach: | They propose to aggregate diverse downstream tasks into a benchmark to assess PLMs' qualities . they evaluate 8 pre-trained masked language models on general and biomedical-specific data . |
| Outcome: | The proposed benchmark assesses pre-trained language models on 20 diversified tasks. |
SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation (2026.eacl-demo)
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Sergio Burdisso, Séverin Baroudi, Yanis Labrak, David Grünert, Pawel Cyrta, Yiyang Chen, Srikanth Madikeri, Esaú Villatoro-tello, Ricard Marxer, Petr Motlicek
| Challenge: | SDialog is an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents. |
| Approach: | They present an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents. |
| Outcome: | SDialog enables more controlled, transparent, and systematic research on conversational systems. |
How Important Is Tokenization in French Medical Masked Language Models? (2024.lrec-main)
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| Challenge: | Word tokenization into subword units has become the prevailing standard in the field of natural language processing (NLP) over recent years . the precise factors contributing to its success remain unclear . |
| Approach: | They propose a tokenization strategy that integrates morpheme-enriched word segmentation into existing tokenization methods. |
| Outcome: | The proposed tokenization strategy outperforms character and word tokenization but the precise factors contributing to its success remain unclear. |
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains (2023.acl-long)
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Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille, Pierre-Antoine Gourraud
| Challenge: | Recent studies have shown that pre-trained language models improve performance on a wide range of NLP tasks. |
| Approach: | They propose to use pre-trained language models to train medical domains on French language to compare performance with specialized ones. |
| Outcome: | The proposed models can take advantage of existing biomedical models in a foreign language by further pre-training them on our targeted data. |
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains (2024.findings-acl)
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Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, Richard Dufour
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. |
| Approach: | They propose an open-source LLM tailored for the biomedical domain that utilizes Mistral as its foundation model and pre-trained on PubMed Central. |
| Outcome: | The proposed model outperforms existing models on a benchmark comprising 10 established medical question-answering tasks in English and is competitive with proprietary models. |