Papers by Gabriel Loiseau
Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization (2025.emnlp-demos)
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| Challenge: | Existing studies on text anonymization prioritize privacy preservation at the expense of utility, relying on reference-based metrics like ROUGE, BERTScore, or METEOR to measure textual fidelity. |
| Approach: | They propose an open-source framework for benchmarking text anonymization methods through the lens of privacy and utility task sensitivity. |
| Outcome: | The proposed framework is open-source and provides a Python library, documentation and tutorials. |
CALE : Concept-Aligned Embeddings for Both Within-Lemma and Inter-Lemma Sense Differentiation (2026.eacl-long)
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| Challenge: | Recent work on Word-in-Context fine-tunes models to investigate lexical meaning but only compares occurrences of the same lemma, limiting the range of captured information. |
| Approach: | They propose an extension to Word-in-Context to include inter-words scenarios by using a dataset and several models on a data set. |
| Outcome: | The proposed models provide efficient multi-purpose representations of lexical meaning that reach best performances in the experiments. |
Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization (2026.findings-acl)
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| Challenge: | Existing methods for anonymizing textual documents lack flexibility to adapt to diverse requirements. |
| Approach: | They propose a task formulation in which anonymization strategies are automatically adapted to specific privacy–utility requirements. |
| Outcome: | The proposed framework achieves better privacy–utility trade-off than existing baselines on open-source language models while remaining computationally efficient and effective on larger closed-source models. |