Recent advancements in Natural Language Processing (NLP) and widely available social media data have made it possible to predict human personalities in various computational applications. In this context, pre-trained Large Language Models (LLMs) have gained recognition for their exceptional performance in NLP benchmarks. However, these models require substantial computational resources, escalating their carbon and water footprint. Consequently, a shift toward more computationally efficient smaller models is observed.
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Habib et al. (2024) studied this question.
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