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National Institute of Informatics (NII), Japan
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Current NLP metrics for "trustworthy" AI in mental health are dangerously misaligned with the actual needs of patients and practitioners.
LLMs judging content aren't as objective as we thought: they're swayed by source labels just like humans, giving "human-authored" content an unfair trust advantage.
A lightweight architecture that distills long textual sequences using visual tokens as dynamic queries boosts LLM performance on 2D table understanding by 23.9%.
Force GNNs to explicitly reason about graph-level concepts and you get state-of-the-art performance in both classification and interpretability.
