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Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka
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New benchmarks reveal that even advanced LLMs struggle with cultural value alignment, but targeted fine-tuning using Sri Lankan values can significantly enhance their performance.
SalAngaBhava reveals that low-resource languages can finally leverage aspect-based sentiment analysis with a robust, annotated dataset tailored for Sinhala.
Land-cover type trumps individual environmental factors as the key predictor of bird diversity in Sri Lanka, with urbanization favoring generalists at the expense of overall richness.
Star ratings can mislead sentiment analysis, with nearly 19% of reviews showing significant disagreement between ratings and text sentiment.
A new, large-scale diachronic corpus for Sinhala, SiDiaC-v.2.0, offers a crucial resource for NLP research on this low-resource language, enabling studies of linguistic change and historical text analysis.
