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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/2501.07853 |
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| _version_ | 1866916565871493120 |
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| author | Ratan, Shobhit Knight, Farley Jerfel, Ghada Ho, Sze Chung |
| author_facet | Ratan, Shobhit Knight, Farley Jerfel, Ghada Ho, Sze Chung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07853 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques Ratan, Shobhit Knight, Farley Jerfel, Ghada Ho, Sze Chung Computation and Language Artificial Intelligence 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. |
| title | Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.07853 |