Multidimensional classification of posts for online course discussion forum curation
This addresses the resource-intensive need for constant updates in online education forums, but the approach is incremental as it builds on existing classification methods.
The paper tackled the problem of costly frequent retraining of Large Language Models (LLMs) for online course discussion forum curation by proposing Bayesian fusion to combine pre-trained LLM scores with a local classifier, resulting in improved performance that is competitive with fine-tuning.
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