4 papers
DivMerge: A divergence-based model merging method for multi-tasking
Brahim Touayouch, Loïc Fosse, Géraldine Damnati +1
Multi-task learning (MTL) is often achieved by merging datasets before fine-tuning, but the growing availability of fine-tuned models has led to new approaches such as model mergin…
Statistical Deficiency for Task Inclusion Estimation
Loïc Fosse, Frédéric Béchet, Benoît Favre +5
Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any t…
Factual Knowledge in Language Models: Robustness and Anomalies under Simple Temporal Context Variations
Hichem Ammar Khodja, Frédéric Béchet, Quentin Brabant +2
This paper explores the robustness of language models (LMs) to variations in the temporal context within factual knowledge. It examines whether LMs can correctly associate a tempor…
TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain
Camille Barboule, Viet-Phi Huynh, Adrien Bufort +3
Despite outstanding processes in many tasks, Large Language Models (LLMs) still lack accuracy when dealing with highly technical domains. Especially, telecommunications (telco) is…