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Philippe Formont

4 accepted papers

2025

Learning Task-Agnostic Representations through Multi-Teacher Distillation

NeurIPS 2025poster

Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differences in architectures, loss functions, input modalities and datasets, each capturing unique aspects of the input. Multi-teacher distillation leverages this dive…

Cited by 0SourceScholar
2025

Statistical Deficiency for Task Inclusion Estimation

ACL 2025long

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 task. Even though transfer learning and multitask learning try to leverage the underlying task space, no well-founded too…

Cited by 0SourcePDFScholar
2024

COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation

ACL 2024long

Assessing the quality of summarizers poses significant challenges—gold summaries are hard to obtain and their suitability depends on the use context of the summarization system. Who is the user of the system, and what do they intend to do with the summary? In response, we propose a novel task-orient…

Cited by 3SourcePDFScholar
2024

When is an Embedding Model More Promising than Another?

NeurIPS 2024poster

Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to perform various downstream tasks. The evaluation of embedding models typically depends on domain-specific empirical approaches utilizing downstream tasks, primar…

Cited by 1SourcePDFScholar