Tootfinder

mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
Marc Marone, Orion Weller, William Fleshman, Eugene Yang, Dawn Lawrie, Benjamin Van Durme
https://arxiv.org/abs/2509.06888

mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
Encoder-only languages models are frequently used for a variety of standard machine learning tasks, including classification and retrieval. However, there has been a lack of recent research for encoder models, especially with respect to multilingual models. We introduce mmBERT, an encoder-only language model pretrained on 3T tokens of multilingual text in over 1800 languages. To build mmBERT we introduce several novel elements, including an inverse mask ratio schedule and an inverse temperature…

How Important Is Preserving Democracy to Ukrainians? A Machine Learning Analysis of Democratic Attitudes: https://benborges.xyz/2025/10/08/how-important-is-preserving-democracy.html

Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples
Alexandra Souly, Javier Rando, Ed Chapman, Xander Davies, Burak Hasircioglu, Ezzeldin Shereen, Carlos Mougan, Vasilios Mavroudis, Erik Jones, Chris Hicks, Nicholas Carlini, Yarin Gal, Robert Kirk
https://arxiv.org/abs/2510.07192…

Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples
Poisoning attacks can compromise the safety of large language models (LLMs) by injecting malicious documents into their training data. Existing work has studied pretraining poisoning assuming adversaries control a percentage of the training corpus. However, for large models, even small percentages translate to impractically large amounts of data. This work demonstrates for the first time that poisoning attacks instead require a near-constant number of documents regardless of dataset size. We co…

MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation
Yilin Mei, Peng Qiu, Wei Zhang, WenChao Zhang, Wenjie Song
https://arxiv.org/abs/2510.04592 ht…

MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation
Recent advances in robotics have been largely driven by imitation learning, which depends critically on large-scale, high-quality demonstration data. However, collecting such data remains a significant challenge-particularly for mobile manipulators, which must coordinate base locomotion and arm manipulation in high-dimensional, dynamic, and partially observable environments. Consequently, most existing research remains focused on simpler tabletop scenarios, leaving mobile manipulation relativel…

Language Bias in Information Retrieval: The Nature of the Beast and Mitigation Methods
Jinrui Yang, Fan Jiang, Timothy Baldwin
https://arxiv.org/abs/2509.06195 https://

Language Bias in Information Retrieval: The Nature of the Beast and Mitigation Methods
Language fairness in multilingual information retrieval (MLIR) systems is crucial for ensuring equitable access to information across diverse languages. This paper sheds light on the issue, based on the assumption that queries in different languages, but with identical semantics, should yield equivalent ranking lists when retrieving on the same multilingual documents. We evaluate the degree of fairness using both traditional retrieval methods, and a DPR neural ranker based on mBERT and XLM-R. A…

Hahahahaha. Pleur toch op zeg.
Wilders: ik vind dat wij een plek aan de onderhandelingstafel verdienen https://nos.nl/l/2589136#UPDATE-90005664

• Wilders wil meepraten over coalitie • Jetten zet in op 'stabiel kabinet'
In dit liveblog lees je het laatste politieke nieuws over de start van de formatie voor een nieuw kabinet.

Dynamics of Infection Spread and Hotspot Growth in Bi-Pathogen Networks
Alyssa Yu, Laura P. Schaposnik
https://arxiv.org/abs/2509.03374 https://arxiv.org/p…

Dynamics of Infection Spread and Hotspot Growth in Bi-Pathogen Networks
Understanding the spatio-temporal evolution of epidemics with multiple pathogens requires not only new theoretical models but also careful analysis of their practical consequences. Building on the Multiplex Bi-Virus Reaction-Diffusion framework (MBRD) introduced in our companion paper, we investigate how the super-infection model (MBRD-SI) and the co-infection model (MBRD-CI) behave under different epidemiological and network conditions. Through numerical experiments, we study the effects of pa…

Spirits in the Stacks: A Ghostly Tour at the Congregational Library & Archives
https://ift.tt/mb4RdeS
ANN: New Book on Matriculture, Shamanism, and the Authority of Women: The Powers That Be Linnéa…
via Input 4 RELCFP

“Een complexe verkiezingsuitslag?”
Dat dacht ik toch niet. Het is heel simpel en welke coalitie er moet komen is ook helder.
Het enige complexe aan het geheel is de verantwoordelijkheidsvaksntie waar de VVD nu eens eindelijk van terug moet komen.
Koolmees: geen eenvoudige opdracht, maar ga snel aan slag https://nos.…

• Wilders wil meepraten over coalitie • Jetten zet in op 'stabiel kabinet'
In dit liveblog lees je het laatste politieke nieuws over de start van de formatie voor een nieuw kabinet.

Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
T. O. Abiola, K. D. Abiodun, O. E. Olumide, O. O. Adebanji, O. Hiram Calvo, Grigori Sidorov
https://arxiv.org/abs/2509.20315

Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
Hope speech language that fosters encouragement and optimism plays a vital role in promoting positive discourse online. However, its detection remains challenging, especially in multilingual and low-resource settings. This paper presents a multilingual framework for hope speech detection using an active learning approach and transformer-based models, including mBERT and XLM-RoBERTa. Experiments were conducted on datasets in English, Spanish, German, and Urdu, including benchmark test sets from …