Papers by Léo Labat

2 papers
The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations (2026.acl-long)

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Challenge: Large Language Models (LLMs) are ubiquitous in modern NLP, but ethical questions have been raised about their use as analysis tools.
Approach: They propose a framework that transforms noisy, multi-topic contributions into argumentative units ready for downstream analysis.
Outcome: The proposed framework can be run locally and transparently with limited resources.
Polyglots or Multitudes? Multilingual LLM Answers to Value-laden Multiple-Choice Questions (2026.eacl-long)

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Challenge: Multiple-choice questions (MCQs) are used to assess knowledge, reasoning abilities, and even values encoded in large language models.
Approach: They propose to test whether multilingual LLMs are consistent in their responses across languages . they also use human-translated questions aligned in 8 European languages to test their robustness .
Outcome: The proposed corpus of questions is aligned in 8 European languages and compared with previous studies.

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