6 citations · 14 across the 20 of their papers we have counts for
20 papers
Adapting Definition Modeling for New Languages: A Case Study on Belarusian
Daniela Kazakouskaya, Timothee Mickus, Janine Siewert
Definition modeling, the task of generating new definitions for words in context, holds great prospect as a means to assist the work of lexicographers in documenting a broader vari…
SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakes
Raúl Vázquez, Timothee Mickus, Elaine Zosa +15
We present the Mu-SHROOM shared task which is focused on detecting hallucinations and other overgeneration mistakes in the output of instruction-tuned large language models (LLMs).…
Your Model is Overconfident, and Other Lies We Tell Ourselves
Timothee Mickus, Aman Sinha, Raúl Vázquez
The difficulty intrinsic to a given example, rooted in its inherent ambiguity, is a key yet often overlooked factor in evaluating neural NLP models. We investigate the interplay an…
Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?
Aman Sinha, Timothee Mickus, Marianne Clausel +2
The success of pretrained language models (PLMs) across a spate of use-cases has led to significant investment from the NLP community towards building domain-specific foundational…
AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling
Mariia Fedorova, Timothee Mickus, Niko Partanen +3
This paper describes the organization and findings of AXOLOTL'24, the first multilingual explainable semantic change modeling shared task. We present new sense-annotated diachronic…
A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives
Zihao Li, Shaoxiong Ji, Timothee Mickus +2
Pretrained language models (PLMs) display impressive performances and have captured the attention of the NLP community. Establishing best practices in pretraining has, therefore, b…
I Have an Attention Bridge to Sell You: Generalization Capabilities of Modular Translation Architectures
Timothee Mickus, Raúl Vázquez, Joseph Attieh
Modularity is a paradigm of machine translation with the potential of bringing forth models that are large at training time and small during inference. Within this field of study,…
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
Timothee Mickus, Elaine Zosa, Raúl Vázquez +5
This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccu…
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?
Shaoxiong Ji, Timothee Mickus, Vincent Segonne +1
Multilingual pretraining and fine-tuning have remarkably succeeded in various natural language processing tasks. Transferring representations from one language to another is especi…
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh +7
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled. The trend goes to modularization, a necessary ste…
Isotropy, Clusters, and Classifiers
Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh
Whether embedding spaces use all their dimensions equally, i.e., whether they are isotropic, has been a recent subject of discussion. Evidence has been accrued both for and against…
Grounded and Well-rounded: A Methodological Approach to the Study of Cross-modal and Cross-lingual Grounding
Timothee Mickus, Elaine Zosa, Denis Paperno
Grounding has been argued to be a crucial component towards the development of more complete and truly semantically competent artificial intelligence systems. Literature has divide…
Why bother with geometry? On the relevance of linear decompositions of Transformer embeddings
Timothee Mickus, Raúl Vázquez
A recent body of work has demonstrated that Transformer embeddings can be linearly decomposed into well-defined sums of factors, that can in turn be related to specific network inp…
"Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics
Vincent Segonne, Timothee Mickus
Definition Modeling, the task of generating definitions, was first proposed as a means to evaluate the semantic quality of word embeddings-a coherent lexical semantic representatio…
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces
Timothee Mickus, Denis Paperno, Mathieu Constant
Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm. We show that they can mathematically be reframed as a sum of vector factors and s…
Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings
Timothee Mickus, Kees van Deemter, Mathieu Constant +1
Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computation…
A Game Interface to Study Semantic Grounding in Text-Based Models
Timothee Mickus, Mathieu Constant, Denis Paperno
Can language models learn grounded representations from text distribution alone? This question is both central and recurrent in natural language processing; authors generally agree…
What Meaning-Form Correlation Has to Compose With
Timothee Mickus, Timothée Bernard, Denis Paperno
Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. We focus on the proposal that compositionality can be assessed…
Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
Timothee Mickus, Denis Paperno, Mathieu Constant
Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations. Building on the distributional hypot…
What do you mean, BERT? Assessing BERT as a Distributional Semantics Model
Timothee Mickus, Denis Paperno, Mathieu Constant +1
Contextualized word embeddings, i.e. vector representations for words in context, are naturally seen as an extension of previous noncontextual distributional semantic models. In th…