# Multidimensional classification of posts for online course discussion forum curation

arXiv: https://arxiv.org/abs/2508.10008 · HTML: https://mlsanity.com/paper/2508.10008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice](https://mlsanity.com/paper/2404.16958/) (2024)
- [Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning](https://mlsanity.com/paper/2104.01113/) (2021)
- [A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing](https://mlsanity.com/paper/1810.07762/) (2018)
- [Adaptable and Reliable Text Classification using Large Language Models](https://mlsanity.com/paper/2405.10523/) (2024)
- [Bayesian Classifier Fusion with an Explicit Model of Correlation](https://mlsanity.com/paper/2106.01770/) (2021)
- [Longitudinal Analysis of Discussion Topics in an Online Breast Cancer Community using Convolutional Neural Networks](https://mlsanity.com/paper/1603.08458/) (2016)
- [The MUIR Framework: Cross-Linking MOOC Resources to Enhance Discussion Forums](https://mlsanity.com/paper/2005.07382/) (2020)
- [Deep Dependency Networks for Multi-Label Classification](https://mlsanity.com/paper/2302.00633/) (2023)

## Abstract (arXiv)

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