Multidimensional classification of posts for online course discussion forum curation
Antonio Leandro Martins Candido, Jose Everardo Bessa Maia

TL;DR
This paper introduces a Bayesian fusion method to improve online course discussion forum classification by combining generic LLM scores with local data classifiers, reducing the need for costly fine-tuning.
Contribution
It presents a novel fusion approach that enhances classification performance without requiring frequent LLM retraining, offering a resource-efficient alternative.
Findings
Fusion outperforms individual classifiers
Comparable to fine-tuned LLMs in accuracy
Reduces retraining costs for online forum curation
Abstract
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
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