Publications

Object-centric Binding in Contrastive Language-Image Pretraining
Rim Assouel
Pietro Astolfi
Florian Bordes
Michal Drozdzal
Adriana Romero Soriano
2025-02-19
ArXiv (preprint)
doi.org
arxiv.org
Object-centric Binding in Contrastive Language-Image Pretraining
Rim Assouel
Pietro Astolfi
Florian Bordes
Michal Drozdzal
Adriana Romero Soriano
Recent advances in vision language models (VLM) have been driven by contrastive models such as CLIP, which learn to associate visual informa… (see more)tion with their corresponding text descriptions. However, these models have limitations in understanding complex compositional scenes involving multiple objects and their spatial relationships. To address these challenges, we propose a novel approach that diverges from commonly used strategies, which rely on the design of hard-negative augmentations. Instead, our work focuses on integrating inductive biases into pre-trained CLIP-like models to improve their compositional understanding without using any additional hard-negatives. To that end, we introduce a binding module that connects a scene graph, derived from a text description, with a slot-structured image representation, facilitating a structured similarity assessment between the two modalities. We also leverage relationships as text-conditioned visual constraints, thereby capturing the intricate interactions between objects and their contextual relationships more effectively. Our resulting model not only enhances the performance of CLIP-based models in multi-object compositional understanding but also paves the way towards more accurate and sample-efficient image-text matching of complex scenes.
2025-02-19
ArXiv (preprint)
arxiv.org
arxiv.org
Making the Write Connections: Linking Writing Support Tools with Writer's Needs
Zixin Zhao
Damien Masson
Young-Ho Kim
Gerald Penn
Fanny Chevalier
This work sheds light on whether and how creative writers' needs are met by existing research and commercial writing support tools (WST). We… (see more) conducted a need finding study to gain insight into the writers' process during creative writing through a qualitative analysis of the response from an online questionnaire and Reddit discussions on r/Writing. Using a systematic analysis of 115 tools and 67 research papers, we map out the landscape of how digital tools facilitate the writing process. Our triangulation of data reveals that research predominantly focuses on the writing activity and overlooks pre-writing activities and the importance of visualization. We distill 10 key takeaways to inform future research on WST and point to opportunities surrounding underexplored areas. Our work offers a holistic and up-to-date account of how tools have transformed the writing process, guiding the design of future tools that address writers' evolving and unmet needs.
2025-02-18
ArXiv (preprint)
doi.org
arxiv.org
Making the Write Connections: Linking Writing Support Tools with Writer Needs
Zixin Zhao
Damien Masson
Young-Ho Kim
Gerald Penn
Fanny Chevalier
2025-02-18
ArXiv (preprint)
doi.org
arxiv.org
Making the Write Connections: Linking Writing Support Tools with Writer's Needs
Zixin Zhao
Damien Masson
Young-Ho Kim
Gerald Penn
Fanny Chevalier
2025-02-18
ArXiv (preprint)
doi.org
arxiv.org
Making the Write Connections: Linking Writing Support Tools with Writer Needs
Zixin Zhao
Damien Masson
Young-Ho Kim
Gerald Penn
Fanny Chevalier
2025-02-18
ArXiv (preprint)
doi.org
arxiv.org
Multilingual Language Model Pretraining using Machine-translated Data
Jiayi Wang
Yao Lu
Maurice Weber
Max Ryabinin
David Ifeoluwa Adelani
Yihong Chen
Raphael Tang
Pontus Stenetorp
2025-02-18
ArXiv (preprint)
doi.org
arxiv.org
Multilingual Language Model Pretraining using Machine-translated Data
Jiayi Wang
Yao Lu
Maurice Weber
Max Ryabinin
David Ifeoluwa Adelani
Yihong Chen
Raphael Tang
Pontus Stenetorp
2025-02-18
ArXiv (preprint)
doi.org
arxiv.org
Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon
Shuang Ni
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization…
2025-02-18
ArXiv (preprint)
arxiv.org
arxiv.org
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives
Leo Schwinn
Yan Scholten
Tom Wollschlager
Sophie Xhonneux
Stephen Casper
Stephan Günnemann
Gauthier Gidel
2025-02-17
ArXiv (preprint)
doi.org
arxiv.org
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives
Leo Schwinn
Yan Scholten
Tom Wollschlager
Sophie Xhonneux
Stephen Casper
Stephan Günnemann
Gauthier Gidel
Misaligned research objectives have considerably hindered progress in adversarial robustness research over the past decade. For instance, an… (see more) extensive focus on optimizing target metrics, while neglecting rigorous standardized evaluation, has led researchers to pursue ad-hoc heuristic defenses that were seemingly effective. Yet, most of these were exposed as flawed by subsequent evaluations, ultimately contributing little measurable progress to the field. In this position paper, we illustrate that current research on the robustness of large language models (LLMs) risks repeating past patterns with potentially worsened real-world implications. To address this, we argue that realigned objectives are necessary for meaningful progress in adversarial alignment. To this end, we build on established cybersecurity taxonomy to formally define differences between past and emerging threat models that apply to LLMs. Using this framework, we illustrate that progress requires disentangling adversarial alignment into addressable sub-problems and returning to core academic principles, such as measureability, reproducibility, and comparability. Although the field presents significant challenges, the fresh start on adversarial robustness offers the unique opportunity to build on past experience while avoiding previous mistakes.
2025-02-17
ArXiv (preprint)
arxiv.org
arxiv.org
Automatic Pruning of Fine-tuning Datasets for Transformer-based Language Models
Sayed Mohammadreza Tayaranian Hosseini
Seyyed Hasan Mozafari
Brett H. Meyer
James J. Clark
Warren Gross
Transformer-based language models have shown state-of-the-art performance on a variety of natural language understanding tasks. To achieve t… (see more)his performance, these models are first pre-trained on general corpus and then fine-tuned on downstream tasks. Previous work studied the effect of pruning the training set of the downstream tasks on the performance of the model on its evaluation set. In this work, we propose an automatic dataset pruning method for the training set of fine-tuning tasks. Our method is based on the model’s success rate in correctly classifying each training data point. Unlike previous work which relies on user feedback to determine subset size, our method automatically extracts training subsets that are adapted for each pair of model and fine-tuning task. Our method provides multiple subsets for use in dataset pruning that navigate the trade-off between subset size and evaluation accuracy. Our largest subset, which we also refer to as the winning ticket subset, is on average
2025-02-17
Proceedings of The 3rd Conference on Lifelong Learning Agents (published)
proceedings.mlr.press