The article presents a novel fine-tuning paradigm called BayesPrompt. It is designed to improve pre-trained language models’ (PLMs) performance on downstream tasks. The existing problem is that prompt-tuning methods fail…
Continue readingThis paper explores the area of sentiment analysis on noisy Bengali texts, a language with limited resources in this realm. A dataset (NC-SentNoB) was manually annotated to identify ten different…
Continue readingThe paper discusses a new approach to target sentiment classification (TSC) leveraging pre-trained language models (PTLMs). The researchers argue that existing PTLM-based models have limitations. They propose a solution that…
Continue readingThe paper explores how to enhance biomedical language models using adapter modules and knowledge graphs. The approach involves partitioning knowledge graphs into smaller subgraphs, fine-tuning adapter modules for each subgraph,…
Continue readingThe article discusses the challenges of high computational cost and high-dimensional representation in pre-trained language models (PLMs) for text representation. To overcome these issues, the authors propose a novel Knowledge…
Continue readingThe article discusses the efficiency of parameter-shared pre-trained language models (PLMs) in resource-constrained environments. Despite the reduction in model storage and memory costs, parameter sharing does not alleviate computational burdens…
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