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Toluwani Aremu

4 accepted papers

2025

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

CVPR 2025highlight

Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corr…

2025

Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles

ACL 2025long

We introduce the concept of the self-referencing causal cycle (abbreviated ReCall )—a mechanism that enables large language models (LLMs) to bypass the limitations of unidirectional causality, which underlies a phenomenon known as the reversal curse. When an LLM is prompted with sequential data, it…

2025

Optimizing Adaptive Attacks against Watermarks for Language Models

ICML 2025spotlight

Large Language Models (LLMs) can be misused to spread unwanted content at scale. Content watermarking deters misuse by hiding messages in content, enabling its detection using a secret *watermarking key*. Robustness is a core security property, stating that evading detection requires (significant) d…

2024

Redefining Contributions: Shapley-Driven Federated Learning

IJCAI 2024poster

Federated learning (FL) has emerged as a pivotal approach in machine learning, enabling multiple participants to collaboratively train a global model without sharing raw data. While FL finds applications in various domains such as healthcare and finance, it is challenging to ensure global model conv…