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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.10008 |
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| _version_ | 1866916897902034944 |
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| author | Candido, Antonio Leandro Martins Maia, Jose Everardo Bessa |
| author_facet | Candido, Antonio Leandro Martins Maia, Jose Everardo Bessa |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multidimensional classification of posts for online course discussion forum curation Candido, Antonio Leandro Martins Maia, Jose Everardo Bessa Computation and Language Machine Learning 68 I.2.7 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 |
| title | Multidimensional classification of posts for online course discussion forum curation |
| topic | Computation and Language Machine Learning 68 I.2.7 |
| url | https://arxiv.org/abs/2508.10008 |