activity
20222026
most citedIn-Context Learning for Extreme Multi-Label Classification

5 citations · 11 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CL2026

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…

cs.CL2026

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…

cs.IR2026

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…

cs.CL20242 cited

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,…

cs.CL20245 cited

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…

cs.CL20231 cited

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…