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
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)
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
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
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
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Shamsuddeen Hassan Muhammad
Nedjma OUSIDHOUM
Idris Abdulmumin
Jan Philip Wahle
Terry Lima Ruas
Meriem Beloucif
Christine de Kock
Nirmal Surange
Daniela Teodorescu
Ibrahim Ahmad
David Ifeoluwa Adelani
Alham Fikri Aji
Felermino Ali
Ilseyar Alimova
Vladimir Araujo
Nikolay Babakov
Naomi Baes
Ana-Maria Bucur
Andiswa Bukula
Guanqun Cao … (see 28 more)
Rodrigo Tufino Cardenas
Rendi Chevi
Chiamaka Ijeoma Chukwuneke
Alexandra Ciobotaru
Daryna Dementieva
Murja Sani Gadanya
Robert Geislinger
Bela Gipp
Oumaima Hourrane
Oana Ignat
Falalu Lawan
Rooweither Mabuya
Rahmad Mahendra
Vukosi Marivate
Andrew Piper
Alexander Panchenko
Charles Henrique Porto Ferreira
Vitaly Protasov
Samuel Rutunda
Manish Shrivastava
Aura Cristina Udrea
Lilian D. A. Wanzare
Sophie Wu
Florian Valentin Wunderlich
Hanif Muhammad Zhafran
Tianhui Zhang
Yi Zhou
Saif M. Mohammad
2025-02-17
ArXiv (preprint)
doi.org
arxiv.org
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Shamsuddeen Hassan Muhammad
Nedjma OUSIDHOUM
Idris Abdulmumin
Jan Philip Wahle
Terry Lima Ruas
Meriem Beloucif
Christine de Kock
Nirmal Surange
Daniela Teodorescu
Ibrahim Ahmad
David Ifeoluwa Adelani
Alham Fikri Aji
Felermino Ali
Ilseyar Alimova
Vladimir Araujo
Nikolay Babakov
Naomi Baes
Ana-Maria Bucur
Andiswa Bukula
Guanqun Cao … (see 28 more)
Rodrigo Tufino Cardenas
Rendi Chevi
Chiamaka Ijeoma Chukwuneke
Alexandra Ciobotaru
Daryna Dementieva
Murja Sani Gadanya
Robert Geislinger
Bela Gipp
Oumaima Hourrane
Oana Ignat
Falalu Lawan
Rooweither Mabuya
Rahmad Mahendra
Vukosi Marivate
Andrew Piper
Alexander Panchenko
Charles Henrique Porto Ferreira
Vitaly Protasov
Samuel Rutunda
Manish Shrivastava
Aura Cristina Udrea
Lilian D. A. Wanzare
Sophie Wu
Florian Valentin Wunderlich
Hanif Muhammad Zhafran
Tianhui Zhang
Yi Zhou
Saif M. Mohammad
2025-02-17
ArXiv (preprint)
doi.org
arxiv.org
Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation
Pedro Vianna
Muawiz Chaudhary
Paria Mehrbod
An Tang
Guy Cloutier
Guy Wolf
Michael Eickenberg
Eugene Belilovsky
Deep neural networks have useful applications in many different tasks, however their performance can be severely affected by changes in the … (see more)data distribution. For example, in the biomedical field, their performance can be affected by changes in the data (different machines, populations) between training and test datasets. To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust models to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks. It is implemented by recalculating batch normalization statistics on test batches. Prior work has focused on analysis with test data that has the same label distribution as the training data. However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure. This presents a risk in applying test time adaptation methods in deployment. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. Our selection scheme is based on two principles that we empirically motivate: (1) later layers of networks are more sensitive to label shift (2) individual features can be sensitive to specific classes. We apply the proposed technique to three classification tasks, including CIFAR10-C, Imagenet-C, and diagnosis of fatty liver, where we explore both covariate and label distribution shifts. We find that our method allows to bring the benefits of TTA while significantly reducing the risk of failure common in other methods, while being robust to choice in hyperparameters.
2025-02-17
Proceedings of The 3rd Conference on Lifelong Learning Agents (published)
doi.org
arxiv.org
Characterizing co-purchased food products with soda, fresh fruits, and fresh vegetables using loyalty card purchasing data in Montréal, Canada, 2015–2017
Hiroshi Mamiya
Kody Crowell
Catherine L. Mah
Amélie Quesnel-Vallée
Aman Verma
David Buckeridge
2025-02-17
The International Journal of Behavioral Nutrition and Physical Activity (published)
doi.org
In-Context Parametric Inference: Point or Distribution Estimators?
Sarthak Mittal
Yoshua Bengio
Nikolay Malkin
Guillaume Lajoie
Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random vari… (see more)ables, incorporating priors and updating beliefs via Bayes' theorem, whereas frequentist methods assume fixed but unknown hypotheses, relying on estimators like maximum likelihood. While extensive research has compared these approaches, the frequentist paradigm of obtaining point estimates has become predominant in deep learning, as Bayesian inference is challenging due to the computational complexity and the approximation gap of posterior estimation methods. However, a good understanding of trade-offs between the two approaches is lacking in the regime of amortized estimators, where in-context learners are trained to estimate either point values via maximum likelihood or maximum a posteriori estimation, or full posteriors using normalizing flows, score-based diffusion samplers, or diagonal Gaussian approximations, conditioned on observations. To help resolve this, we conduct a rigorous comparative analysis spanning diverse problem settings, from linear models to shallow neural networks, with a robust evaluation framework assessing both in-distribution and out-of-distribution generalization on tractable tasks. Our experiments indicate that amortized point estimators generally outperform posterior inference, though the latter remain competitive in some low-dimensional problems, and we further discuss why this might be the case.
2025-02-17
ArXiv (preprint)
arxiv.org
arxiv.org
In-Context Parametric Inference: Point or Distribution Estimators?
Sarthak Mittal
Yoshua Bengio
Nikolay Malkin
Guillaume Lajoie
Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random vari… (see more)ables, incorporating priors and updating beliefs via Bayes' theorem, whereas frequentist methods assume fixed but unknown hypotheses, relying on estimators like maximum likelihood. While extensive research has compared these approaches, the frequentist paradigm of obtaining point estimates has become predominant in deep learning, as Bayesian inference is challenging due to the computational complexity and the approximation gap of posterior estimation methods. However, a good understanding of trade-offs between the two approaches is lacking in the regime of amortized estimators, where in-context learners are trained to estimate either point values via maximum likelihood or maximum a posteriori estimation, or full posteriors using normalizing flows, score-based diffusion samplers, or diagonal Gaussian approximations, conditioned on observations. To help resolve this, we conduct a rigorous comparative analysis spanning diverse problem settings, from linear models to shallow neural networks, with a robust evaluation framework assessing both in-distribution and out-of-distribution generalization on tractable tasks. Our experiments indicate that amortized point estimators generally outperform posterior inference, though the latter remain competitive in some low-dimensional problems, and we further discuss why this might be the case.
2025-02-17
ArXiv (preprint)
doi.org
arxiv.org