Large Language Models (LLMs) are proposed as tools for high-throughput, deep phenotyping of psychiatric disorders. Applied to electronic health records, LLMs could in principle extract patient symptoms, outcome trajectories, risk factors, and treatment history at scale and these, when combined with increasingly availab...
S. Lock, J. Boisson, L. M. Evans et al.· medRxiv· 0 citations
Word-in-Context (WiC) remains challenging for language models, despite recent progress on lexical-semantic tasks. We hypothesise that this difficulty arises not only from comparing two contextual uses of a word, but also from the absence of an explicit sense inventory that specifies the relevant level of semantic granu...
Yi Zhou, Kiamehr Rezaee, D. Bollegala et al.· 0 citations
A data-centric analysis of semantic knowledge acquisition in word embeddings, focusing on word analogy and semantic similarity shows that, for relational semantics, training-data quality outweighs quantity, and that simple proxy models remain a practical, interpretable tool for efficient data selection.
Aishwarya Jadhav, Mark Anderson, J. Camacho-Collados et al.· Neural computing & applicati...· 0 citations
It is found that linguistic proximity itself introduces errors: closely related language pairs tend to perform worse, reflecting the challenge of semantic discrimination due to lexical overlap, and unlike other tasks where language distance poses additional challenges, it is found that linguistic proximity itself intro...
Marta Vázquez Abuín, José Camacho-Collados, Marcos García· Annual Meeting of the Associ...· 0 citations
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