The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future

• URL: http://arxiv.org/abs/2506.17700v1
• Date: Sat, 21 Jun 2025 12:25:37 GMT
• Title: The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future
• Authors: Summra Saleem, Muhammad Nabeel Asim, Shaista Zulfiqar, Andreas Dengel,
• Abstract summary: Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP)<n>The idea of prompt engineering and subsequent optimization strategies with LLMs has emerged as a particularly impactful trend to yield a substantial performance boost across diverse NLP tasks.<n>This paper provides unique and comprehensive insights about the potential of diverse prompt optimization strategies.
• Score: 5.001689778344014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by automating traditional labor-intensive tasks and consequently accelerated the development of computer-aided applications. As researchers continue to advance this field with the introduction of novel language models and more efficient training/finetuning methodologies, the idea of prompt engineering and subsequent optimization strategies with LLMs has emerged as a particularly impactful trend to yield a substantial performance boost across diverse NLP tasks. To best of our knowledge numerous review articles have explored prompt engineering, however, a critical gap exists in comprehensive analyses of prompt optimization strategies. To bridge this gap this paper provides unique and comprehensive insights about the potential of diverse prompt optimization strategies. It analyzes their underlying working paradigms and based on these principles, categorizes them into 11 distinct classes. Moreover, the paper provides details about various NLP tasks where these prompt optimization strategies have been employed, along with details of different LLMs and benchmark datasets used for evaluation. This comprehensive compilation lays a robust foundation for future comparative studies and enables rigorous assessment of prompt optimization and LLM-based predictive pipelines under consistent experimental settings: a critical need in the current landscape. Ultimately, this research will centralize diverse strategic knowledge to facilitate the adaptation of existing prompt optimization strategies for development of innovative predictors across unexplored tasks.

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