6 papers · 1 filter
VLURes: Benchmarking VLM Visual and Linguistic Understanding in Low-Resource Languages
Jesse Atuhurra, Iqra Ali, Tomoya Iwakura +2
Vision Language Models (VLMs) are pivotal for advancing perception in intelligent agents. Yet, evaluation of VLMs remains limited to predominantly English-centric benchmarks in whi…
Investigating Neurons and Heads in Transformer-based LLMs for Typographical Errors
Kohei Tsuji, Tatsuya Hiraoka, Yuchang Cheng +2
This paper investigates how LLMs encode inputs with typos. We hypothesize that specific neurons and attention heads recognize typos and fix them internally using local and global c…
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization
Kohei Tsuji, Tatsuya Hiraoka, Yuchang Cheng +1
NLP datasets may still contain annotation errors, even when they are manually annotated. Researchers have attempted to develop methods to automatically reduce the adverse effect of…
Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
Jesse Atuhurra, Iqra Ali, Tatsuya Hiraoka +3
Large language models (LLMs) have increased interest in vision language models (VLMs), which process image-text pairs as input. Studies investigating the visual understanding abili…
Downstream Task-Oriented Neural Tokenizer Optimization with Vocabulary Restriction as Post Processing
Tatsuya Hiraoka, Tomoya Iwakura
This paper proposes a method to optimize tokenization for the performance improvement of already trained downstream models. Our method generates tokenization results attaining lowe…
Tokenization Preference for Human and Machine Learning Model: An Annotation Study
Tatsuya Hiraoka, Tomoya Iwakura
Is preferred tokenization for humans also preferred for machine-learning (ML) models? This study examines the relations between preferred tokenization for humans (appropriateness a…