小数据微调能提升短答案评分效果，尤其对闭源模型更明显。
Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading
- 用少量数据微调模型，结合提示工程提升评分准确率。
- 闭源模型微调后表现优于零样本提示，开源自研模型效果有限。
- 合成数据可显著提升开源模型性能，适合资源有限的研究者。
近期研究聚焦于使用大语言模型（LLMs）通过提示工程和少样本提示实现自动短答案评分（ASAG），而传统微调因需大规模算力难以普及。新出现的闭源模型微调服务仅需100个样本即可获得良好效果，而基于量化低秩自适应（QLORA）的开源模型可在消费级显卡上完成微调。本文评估了这两种微调方法在结构化输出（JSON）下的表现，发现小数据微调对Llama开源模型帮助有限，但对OpenAI闭源模型能显著超越少样本提示基线。尽管评估范围有限，结果表明微调收益可能受领域主题影响。此外，通过引入大量低成本生成的合成数据，Llama 3.1 8B-Instruct模型性能得到显著提升。
原文摘要 · Abstract (English)
Research to improve Automated Short Answer Grading has recently focused on Large Language Models (LLMs) with prompt engineering and no- or few-shot prompting to achieve best results. This is in contrast to the fine-tuning approach, which has historically required large-scale compute clusters inaccessible to most users. New closed-model approaches such as OpenAI's fine-tuning service promise results with as few as 100 examples, while methods using open weights such as quantized low-rank adaptive (QLORA) can be used to fine-tune models on consumer GPUs. We evaluate both of these fine-tuning methods, measuring their interaction with few-shot prompting for automated short answer grading (ASAG) with structured (JSON) outputs. Our results show that finetuning with small amounts of data has limited utility for Llama open-weight models, but that fine-tuning methods can outperform few-shot baseline instruction-tuned LLMs for OpenAI's closed models. While our evaluation set is limited, we find some evidence that the observed benefits of finetuning may be impacted by the domain subject matter. Lastly, we observed dramatic improvement with the LLama 3.1 8B-Instruct open-weight model by seeding the initial training examples with a significant amount of cheaply generated synthetic training data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。