Papers by Nicolas Garneau

5 papers
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge (2025.naacl-long)

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Challenge: Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet lack a robust methodology to dissect these phenomena comprehensively.
Approach: They propose a multilingual dataset centered on food-related cultural facts and variations in food practices.
Outcome: The proposed model incorporates cultural context significantly and improves its ability to access cultural knowledge.
Defining Knowledge: Bridging Epistemology and Large Language Models (2024.emnlp-main)

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Challenge: Existing literature on large language models (LLMs) define knowledge as a fact if it correctly completes a cloze sentence . but the predictions of semantically equivalent clozing sentences are inconsistent .
Approach: They review standard definitions of knowledge in epistemology and formalize interpretations applicable to LLMs.
Outcome: The authors compare the preferences of philosophers and computer scientists in terms of knowledge definitions and evaluation protocols for testing knowledge in accordance with the most relevant definitions.
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well (2020.lrec-1)

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Challenge: Existing methods for fully unsupervised cross-lingual mapping of word embeddings are available to achieve such a mapping .
Approach: They reproduce the experiments of Artetxe and Sgaard (2018) . they propose a robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.
Outcome: The proposed method is feasible with minor assumptions, and it is able to be replicated in four languages.
MuLan: A Study of Fact Mutability in Language Models (2024.naacl-short)

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Challenge: Pretrained and large language models encode factual knowledge, but factual information changes over time and mutates with the passage of time.
Approach: They propose to use a model to evaluate the ability of English language models to anticipate time-contingency by comparing their models to a benchmark model.
Outcome: The proposed model can predict the president of a country or the winner of sa championship in time, but it is difficult to update them due to their mutability.
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development (2023.acl-long)

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Challenge: In this study, we examine the performance of legal-oriented pre-trained language models.
Approach: They conduct a detailed analysis on the performance of legal-oriented pre-trained language models by examining their original objective, acquired knowledge, and legal language understanding capacities.
Outcome: The results show that the models' size and pre-training corpora are important for the development of domain-specific models.

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