The automatic curation of discussion forums in online courses requires constant updates, making frequent retraining of Large Language Models (LLMs) a resource-intensive process. To circumvent the need for costly fine-tuning, this paper proposes and evaluates the use of Bayesian fusion. The approach combines the multidimensional classification scores of a pre-trained generic LLM with those of a classifier trained on local data. The performance comparison demonstrated that the proposed fusion improves the results compared to each classifier individually, and is competitive with the LLM fine-tuning approach
@article{arxiv.2508.10008,
title = {Multidimensional classification of posts for online course discussion forum curation},
author = {Antonio Leandro Martins Candido and Jose Everardo Bessa Maia},
journal= {arXiv preprint arXiv:2508.10008},
year = {2025}
}