As machine learning systems are increasingly deployed on large-scale data, the demand for interpretable and scalable explanation methods has become critical. Existing Explainable AI techniques, particularly for clustering, often struggle with scalability and generalization beyond small, single-node environments. This p...
While large language models (LLMs) have achieved remarkable linguistic capabilities, a profound question lingers at their core: do these models truly comprehend context or simply excel at pattern matching on an unprecedented scale? Contextual understanding in LLMs refers to the ability to correctly extract relevant inf...
Subavarshana Arumugam, Mamta Nallaretnam, K. Wickramasinghe et al.· 0 citations
EnSiTa is presented, a trilingual multi-domain parallel dataset and benchmark for English, Sinhala and Tamil, and is the most extensive systematically documented multi-domain parallel data creation and benchmarking effort for low-resource MT.
Surangika Ranathunga, Nisansa de Silva, Aloka Fernando et al.· 1 citation· ⚡1
Large Language Models (LLMs) demonstrate impressive performance across diverse NLP tasks, yet their ability to exhibit genuine contextual understanding remains uncertain. Traditional evaluation metrics such as perplexity, BiLingual Evaluation Understudy (BLEU), or surface-level accuracy fail to reveal how well LLMs ext...
Subavarshana Arumugam, Mamta Nallaretnam, K. Wickramasinghe et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
