Downstream NLP Task

Downstream NLP tasks involve adapting pre-trained language models (LLMs) to specific applications, focusing on improving efficiency, accuracy, and robustness. Current research emphasizes techniques like parameter-efficient fine-tuning, data augmentation (including knowledge-based methods), and innovative prompting strategies to optimize LLMs for diverse tasks such as translation, sentiment analysis, and question answering. These advancements are crucial for broadening the accessibility and applicability of LLMs across various domains, while also addressing challenges like data scarcity, computational cost, and potential biases.