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The University of Tokyo, Tohoku University
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VLMs misinterpret spatial deictic expressions, failing to match human-like selection based on object proximity.
Even Japanese-specific LLMs fail to grasp kanji readings, revealing a significant shortcoming in their linguistic understanding.
LLMs can grasp negation to some extent, but their ability to recognize negation scope is surprisingly inconsistent and format-dependent.
LLMs may completely bypass the neglect-zero effect, challenging assumptions about their alignment with human cognitive biases.
Code-switching can degrade information retrieval performance by up to 27%, revealing a critical blind spot in current multilingual models.
VLMs already contain a rich latent space of aesthetic features that can be unlocked for personalized image ranking with just a linear readout, no fine-tuning needed.
Adapting Labovian narrative analysis to Japanese reveals the challenges and opportunities in cross-linguistic qualitative research, highlighting the need for language-specific guidelines.
NeuronMoE slashes multilingual LLM parameter counts by 40% without sacrificing performance, by cleverly allocating experts based on neuron-level language specialization rather than blunt layer-level assignments.
