RFOP: Rethinking Fusion and Orthogonal Projection for Face-Voice Association

Recent research highlights the persistent bias in multilingual large language models (LLMs) towards Islam, revealing that these models often misrepresent religious contexts, particularly when responding in Bengali compared to English. The study introduces the BRAND dataset, which focuses on major South Asian religions and aims to improve bias detection in AI systems.

Recent research has shown that large language models (LLMs) can effectively differentiate between grammatical and ungrammatical sentences, revealing that various types of syntactic agreement, such as subject-verb and determiner-noun, utilize overlapping units within these models. This study involved a functional localization approach to identify the responsive units across 67 English syntactic phenomena in seven open-weight models.

A new dataset named Reveal-Bangla has been introduced, focusing on cross-lingual multi-step reasoning evaluation in Bangla, derived from the English Reveal dataset. This dataset includes both binary and non-binary question types and aims to assess the reasoning capabilities of multilingual small language models in Bangla compared to English.

The introduction of PIXEL-M4 marks a significant advancement in multilingual pretraining for pixel language models, which operate directly on images of rendered text. This model has been pretrained on four diverse languages: English, Hindi, Ukrainian, and Simplified Chinese, showcasing its ability to outperform English-only models in tasks involving non-Latin scripts.