Using Large Language Models for the Assessment of Sustainable Forest Investment Projects

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dc.contributor.authorJannibelli, Maria Letizia
dc.contributor.authorLuo, Jiayu
dc.contributor.authorSprenkamp, Kilian
dc.contributor.authorZavolokina, Liudmila
dc.date.accessioned2024-12-26T21:08:25Z
dc.date.available2024-12-26T21:08:25Z
dc.date.issued2025-01-07
dc.description.abstractThe integration of Large Language Models (LLMs) into the assessment processes of sustainable forest investment projects is a compelling prospect, given the limitations present in manual assessment. This paper examines how such an LLM-based assessment tool can be designed and whether such a tool can serve as a viable alternative to human experts in this task through the development and subsequent evaluation of a prototype. The analysis shows that the use of retrieval augmented generation to extract and summarize relevant information from project documents is promising but reveals challenges in the use of LLMs for more complex analysis and grading tasks. Design principles and possible steps for further development of the tool are proposed.
dc.format.extent10
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other413be0d5-92eb-40df-97f6-d3b38b3439f0
dc.identifier.urihttps://hdl.handle.net/10125/109408
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAI Assistants and Generative AI for Knowledge Creation, Retention, and Use
dc.subjectautomated assessment, information retrieval, large language models, retrieval augmented generation, sustainable forest investments
dc.titleUsing Large Language Models for the Assessment of Sustainable Forest Investment Projects
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage4662

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