activity
20162025
most citedIn-Context Learning for Extreme Multi-Label Classification

5 citations · 26 across the 18 of their papers we have counts for

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15 papers · 1 filter

cs.CL20241 cited

ChocoLlama: Lessons Learned From Teaching Llamas Dutch

Matthieu Meeus, Anthony Rathé, François Remy +3

While Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, their performance often lags in lower-resource, non-English…

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.CL2023

Flexible Model Interpretability through Natural Language Model Editing

Karel D'Oosterlinck, Thomas Demeester, Chris Develder +1

Model interpretability and model editing are crucial goals in the age of large language models. Interestingly, there exists a link between these two goals: if a method is able to s…

cs.CL20231 cited

Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study

Maarten De Raedt, Semere Kiros Bitew, Fréderic Godin +2

The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-l…

cs.CL20234 cited

Career Path Prediction using Resume Representation Learning and Skill-based Matching

Jens-Joris Decorte, Jeroen Van Hautte, Johannes Deleu +2

The impact of person-job fit on job satisfaction and performance is widely acknowledged, which highlights the importance of providing workers with next steps at the right time in t…