Description
Local Planning Authorities (LPAs) in the UK rely on written representations from the community to inform their Local Plans which outline development needs for their area. With an average of 2000 representations per consultation and 4 rounds of consultation per Local Plan, the volume of information can be overwhelming for both LPAs and the Planning Inspectorate tasked with examining the legality and soundness of plans. In this study, we investigate the potential for Large Language Models (LLMs) to streamline representation analysis.
We find that LLMs have the potential to significantly reduce the time and effort required to analyse representations, with simulations on historical Local Plans projecting a reduction in processing time by over 30%, and experiments showing classification accuracy of up to 90%.
In this presentation, we discuss our experimental process which used a distributed experimentation environment with Jupyter Lab and cloud resources to evaluate the performance of the BERT, RoBERTa, DistilBERT, and XLNet models. We also discuss the design and prototyping of web applications to support the aided processing of representations using Voilà, FastAPI, and React. Finally, we highlight successes and challenges encountered and suggest areas for future improvement.