5 citations · 14 across the 12 of their papers we have counts for
13 papers
Online Class Posteriors for Interpretable and Controllable Discrete Diffusion
Felix Koulischer, Gianni Van De Velde, François Remy +2
Discrete diffusion models remain far less understood than their continuous counterparts, despite recent breakthroughs across modalities, most notably in text. Leveraging tools from…
ClinicalAligner26AM: A Cross-Lingual Aligner for Dataset Translation; Evidences from the MultiClinCorpus Shared Task
François Remy
Word-level cross-lingual alignment is central to annotation projection, translation auditing, and cross-lingual faithfulness estimation, yet existing neural aligners are rarely ada…
ClinicalEncoder26AM: A Multlilingual Diagnosable ColBERT Model; Evidences from the MultiClinNER Shared Task
François Remy
ClinicalEncoder26AM is a multilingual Diagnosable ColBERT for clinical and biomedical texts, which aligns at multiple levels its token-level semantic with ClinicalMap25, a clinical…
Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference
François Remy
Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failures and curate the training evid…
Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP
François Remy, Pieter Delobelle, Hayastan Avetisyan +3
The development of monolingual language models for low and mid-resource languages continues to be hindered by the difficulty in sourcing high-quality training data. In this study,…
In-Context Learning for Extreme Multi-Label Classification
Karel D'Oosterlinck, Omar Khattab, François Remy +3
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precis…
BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights
François Remy, Kris Demuynck, Thomas Demeester
In this study, we investigate the potential of Large Language Models to complement biomedical knowledge graphs in the training of semantic models for the biomedical and clinical do…
Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation
François Remy, Pieter Delobelle, Bettina Berendt +2
Training monolingual language models for low and mid-resource languages is made challenging by limited and often inadequate pretraining data. In this study, we propose a novel mode…
Boosting Adverse Drug Event Normalization on Social Media: General-Purpose Model Initialization and Biomedical Semantic Text Similarity Benefit Zero-Shot Linking in Informal Contexts
François Remy, Simone Scaboro, Beatrice Portelli
Biomedical entity linking, also known as biomedical concept normalization, has recently witnessed the rise to prominence of zero-shot contrastive models. However, the pre-training…
Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge
François Remy, Thomas Demeester
Background: More than 400,000 biomedical concepts and some of their relationships are contained in SnomedCT, a comprehensive biomedical ontology. However, their concept names are n…
Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning
François Remy, Alfiya Khabibullina, Thomas Demeester
This paper shines a light on the potential of definition-based semantic models for detecting idiomatic and semi-idiomatic multiword expressions (MWEs) in clinical terminology. Our…
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
Karel D'Oosterlinck, François Remy, Johannes Deleu +7
Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to impr…
BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)
François Remy, Kris Demuynck, Thomas Demeester
This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies op…