Using large language models for the assessment of sustainable forest investment projects

Abstract

The 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.