Portrait of Arsène Fansi Tchango

Arsène Fansi Tchango

arsene.fansi.tchango@mila.quebec
Machine Learning Manager, Applied Machine Learning Research

Publications

CLIP Is Shortsighted: Paying Attention Beyond the First Sentence
Marc-Antoine Lavoie
Anas Mahmoud
Aldo Zaimi
Arsène Fansi Tchango
Steven L. Waslander
CLIP models learn transferable multi-modal features via image-text contrastive learning on internet-scale data. They are widely used in zero… (see more)-shot classification, multi-modal retrieval, text-to-image diffusion, and as image encoders in large vision-language models. However, CLIP’s pretraining is dominated by images paired with short captions, biasing the model toward encoding simple descriptions of salient objects and leading to coarse alignment on complex scenes and dense descriptions. While recent work mitigates this by fine-tuning on small-scale long-caption datasets, we identify an important common bias: both human- and LLM-generated long captions typically begin with a one-sentence summary followed by a detailed description. We show that this acts as a shortcut during training, concentrating attention on the opening sentence and early tokens and weakening alignment over the rest of the caption. To resolve this, we introduce DeBias-CLIP, which removes the summary sentence during training and applies sentence sub-sampling and text token padding to distribute supervision across all token positions. DeBias-CLIP achieves state-of-the-art long-text retrieval, improves short-text retrieval, and is less sensitive to sentence order permutations. It is a drop-in replacement for Long-CLIP with no additional trainable parameters.
2025-12-31
IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept (Highlight))
doi.org
arxiv.org
AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements
Adriana Eufrosina Bora
Pierre-Luc St-Charles
Mirko Bronzi
Arsène Fansi Tchango
Bruno Rousseau
Kerrie Mengersen
Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of gove… (see more)rnment oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solution for the automatic analysis and summarization of documents, recognizing concrete modern slavery countermeasures taken by companies and differentiating those from vague claims remains a challenging task. To help evaluate and fine-tune LLMs for the assessment of corporate statements, we introduce a dataset composed of 5,731 modern slavery statements taken from the Australian Modern Slavery Register and annotated at the sentence level. This paper details the construction steps for the dataset that include the careful design of annotation specifications, the selection and preprocessing of statements, and the creation of high-quality annotation subsets for effective model evaluations. To demonstrate our dataset's utility, we propose a machine learning methodology for the detection of sentences relevant to mandatory reporting requirements set by the Australian Modern Slavery Act. We then follow this methodology to benchmark modern language models under zero-shot and supervised learning settings.
2025-04-22
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net