对比多种微调方法，提升小模型语法正确性评估效率。
Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques
- 用三种微调法优化小模型语法判断能力。
- LoRA使内存和迭代时间减少超50%，还提升部分方法准确率。
- 适合想高效训练大模型但资源有限的研究者。
本研究在CoLA数据集上探索了使用Vanilla-Fine-Tuning（VFT）、Pattern-Based-Fine-Tuning（PBFT）和参数高效微调（PEFT）技术（如低秩适应LoRA）对Open Pre-trained Transformer（OPT-125M）进行语法可接受性任务微调。实验表明，虽然VFT达到最高准确率81.2%，但LoRA显著降低内存占用和迭代时间超过50%，且在PBFT场景中进一步提升准确率。上下文蒸馏（CD）虽计算高效，但准确率仅约31%。研究结果有助于降低大语言模型的使用门槛，推动其普及。
原文摘要 · Abstract (English)
This study explores the fine-tuning (FT) of the Open Pre-trained Transformer (OPT-125M) for grammatical acceptability tasks using the CoLA dataset. By comparing Vanilla-Fine-Tuning (VFT), Pattern-Based-Fine-Tuning (PBFT), and Parameter-Efficient Fine-Tuning techniques (PEFT) like Low-Rank Adaptation (LoRA), we demonstrate significant improvements in computational efficiency while maintaining high accuracy. Our experiments reveal that while VFT achieves the highest accuracy (81.2%), LoRA enhancing FT by reducing memory usage and iteration time by more than 50%, and increases accuracy in PBFT case. Context Distillation (CD), though computationally efficient, underperformed with accuracy around 31%. Our findings contribute to democratizing access to large language models (LLM) by reducing computational barriers.
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