Papers by Thomas Bailleux
CONTOR: Benchmarking Strategies for Completing Ontologies with Plausible Missing Rules (2024.findings-emnlp)
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| Challenge: | Existing evaluations focus on distinguishing held-out ontologies from randomly corrupted ones, which often makes the task unrealistically easy. |
| Approach: | They propose to use the common description logic syntax for encoding ontology rules to test their effectiveness on manually annotated hard negatives. |
| Outcome: | The proposed models are compared with existing models and have been evaluated on different ontologies. |
Grouping Entities with Shared Properties using Multi-Facet Prompting and Property Embeddings (2025.emnlp-main)
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| Challenge: | Methods for learning taxonomies from data are well-studied, but it is difficult to use them in large domains. |
| Approach: | They propose to use LLMs to describe the different properties that are satisfied by each entity individually and then use pre-trained embeddings to cluster these properties. |
| Outcome: | The proposed model can be used to describe the properties of the entities and group them into clusters. |
Credal Concept Bottleneck Models for Epistemic–Aleatoric Uncertainty Decomposition (2026.acl-long)
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| Challenge: | Existing models face challenges when dealing with uncertainty. |
| Approach: | They propose a framework that decomposes concept uncertainty by construction . epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks disagreement . |
| Outcome: | The proposed framework decomposes concept uncertainty by construction . epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks disagreement . |
Explanation Quality Assessment as Ranking with Listwise Rewards (2026.findings-acl)
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| Challenge: | a new approach to explanation quality assessment is to rank explanations by relative quality . standard reward objectives do not preserve graded distinctions well enough for policy optimization . |
| Approach: | They reformulate explanation quality assessment as a ranking problem instead of a generation problem . they train listwise and pairwise ranking models to preserve ordinal structure . |
| Outcome: | The proposed model outperforms regression on score separation and performance on listwise and pairwise models. |