PromptMaster: Engineering Essentials and Basic NLP Tools

Abstract

Prompt Engineering (PE) and Large Language Models (LLM) are important developments in Natural Language Processing (NLP) research. This technique is key for crafting effective prompts that shape how the language model behaves. It is widely applied across various NLP tasks. Research has mainly focused on creating efficient prompts to boost performance in different applications, including chatbots, sentiment analysis, and text summarization. However, some fundamental questions remain unanswered, such as whether this work supports basic NLP or linguistic research in the context of PE and LLM. In traditional NLP, basic tools and techniques, such as part-of-speech taggers, named entity recognition, and morphological analyzers, are crucial for understanding language. Developing such tools remains a challenging issue, particularly for resource-sparce languages. In this paper, we are try to address this question. We have chosen Bengali as the language and have employed language models such as ChatGPT to tackle these challenges. For our experiments, we used publicly available datasets and the results were surprising when compared with the latest state-of-the-art models. We also identified the need to develop new prompts to fulfill basic requirements.