2 citations · 5 across the 27 of their papers we have counts for
6 papers · 2 filters
End-to-end Planner Training for Language Modeling
Nathan Cornille, Florian Mai, Jingyuan Sun +1
Through end-to-end training to predict the next token, LLMs have become valuable tools for various tasks. Enhancing their core training in language modeling can improve numerous do…
Learning to Plan Long-Term for Language Modeling
Florian Mai, Nathan Cornille, Marie-Francine Moens
Modern language models predict the next token in the sequence by considering the past text through a powerful function such as attention. However, language models have no explicit…
"Image, Tell me your story!" Predicting the original meta-context of visual misinformation
Jonathan Tonglet, Marie-Francine Moens, Iryna Gurevych
To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistenc…
Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-att…
Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson +1
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework desi…
Learning to Plan for Language Modeling from Unlabeled Data
Nathan Cornille, Marie-Francine Moens, Florian Mai
By training to predict the next token in an unlabeled corpus, large language models learn to perform many tasks without any labeled data. However, their next-token-prediction objec…