用贝叶斯融合提升在线课程论坛自动整理效果
Multidimensional classification of posts for online course discussion forum curation
- 融合预训练大模型与本地数据分类器的多维评分
- 性能优于单一分类器，接近微调大模型效果
- 适合资源有限但需持续更新的在线教育场景
在线课程讨论区的自动整理需要频繁更新，导致大语言模型（LLM）频繁微调，成本高昂。为避免这一问题，本文提出并评估了贝叶斯融合方法：将通用预训练大模型的多维度分类得分，与基于本地数据训练的分类器结果进行融合。实验表明，该融合方法在性能上优于任一独立分类器，且与直接微调大模型的方法相比具有竞争力，有效降低了持续维护成本。
原文摘要 · Abstract (English)
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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