Paper Title
FROM CORE NLP TO GENERATIVE POWERHOUSES: A WEB-BASED PROPOSAL GENERATION AND MANAGEMENT SYSTEM LEVERAGING THE EVOLUTION OF LANGUAGE MODELS

Abstract
Request for Proposal (RFP) responses are a crucial element for businesses seeking new projects and securing valuable contracts. However, it can take a lot of time and resources to develop excellent proposals that effectively satisfy complex RFP requirements. This paper explores how WinOpps, a comprehensive proposal management system, leverages Large Language Models (LLMs) to address these challenges. Complex natural language processing (NLP) tasks can be carried out by LLMs, a type of artificial intelligence that has been trained on large text and code datasets. WinOpps integrates various LLM functionalities to streamline the proposal writing process, improve efficiency, and enhance proposal quality. We detail how WinOpps utilizes LLMs for tasks such as text extraction from RFP documents, proposal template generation, content search and retrieval, and content generation. User studies and a case study demonstrate that WinOpps with LLMs can significantly reduce proposal turnaround time, improve proposal quality, and increase win rates. The broader implications of LLM integration within WinOpps are discussed, highlighting the potential for advancements in domain-specific LLMs, personalized proposal tailoring, and real-time feedback on drafts. Finally, we explore how WinOpps can stay at the forefront of the evolving LLM landscape by incorporating explainable LLMs, fostering human-LLM collaboration, and leveraging external data sources. By embracing these future directions, WinOpps has the potential to transform the proposal writing process, empowering businesses to compete more effectively and achieve greater success.