An approach for assisted outcome detection and validation using llm for foresight applications

The increasing complexity of foresight tasks demands innovative methods for the detec-tion, analysis, and validation of outcomes to ensure reliable and actionable insights. Thisstudy explores the potential of Large Language Models (LLMs) to assist in foresight applica-tions within rapidly evolving and high-tech domains. The study is using a simplified versionof a known foresight framework, adapted to the specific domain of “Unmanned Systems andCommunication Technologies in the Ukraine-Russia War.” The proposed approach inte-grates natural language processing (NLP) capabilities with system analysis methods and do-main-oriented foresight methodology. Special attention is given to the detection and valida-tion of technological application "outcomes," including immediate results, long-term im-pacts, and trends within the technology lifecycle. Leveraging LLMs enables the processing oflarge volumes of textual data in the context of analyzing and modeling cyclical phenomena.One notable outcome of this study is the identification of "associative inferences"-- predic-tions derived through pattern recognition and latent connections within the data. While theseinsights may not always be explicitly represented in the text, they create potential for furtherresearch in this area. Such forecasts emphasize the capability of LLMs to contribute to fore-sight methods not only by extracting explicit information but also by generating novel inter-pretations that hold promise for scenario modeling. The study also addresses the challengesof configuring models and designing prompts, which significantly influence the quality andrelevance of the extracted information. Employing various model configurations allows forimproved result accuracy and broader topic coverage. Future research is suggested to focuson exploring the potential of LLMs for generating implicit inferences (predictions) and inte-grating these models into other stages of the foresight process. Specifically, this includes stag-es such as SWOT and STEEP analyses and supporting scenario development. Building onthe achieved results, LLMs can significantly enhance the methodologies of foresight imple-mentation in complex and rapidly changing domains.