PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching

• URL: http://arxiv.org/abs/2312.05621v2
• Date: Mon, 07 Oct 2024 03:41:27 GMT
• Title: PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
• Authors: Zhenting Qi, Xiaoyu Tan, Shaojie Shi, Chao Qu, Yinghui Xu, Yuan Qi,
• Abstract summary: Low-Rank Adaptation (LoRA) has become a promising alternative to instruction fine-tuning. PILLOW aims to improve LoRA's performance by a discrimination-based LLM ability. PILLOW exhibits commensurate performance on various evaluation metrics compared with typical instruction fine-tuning methods.
• Score: 20.607323649079845
• License:
• Abstract: Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks. Nonetheless, this technique often necessitates substantial computational resources, making it impractical for deployment by individuals or small-scale entities. Recently, Low-Rank Adaptation (LoRA) has become a promising alternative, offering high capabilities on par with full tuning with reduced resource overhead. However, attaining satisfactory performance through the fine-tuning of LoRA is a non-trivial challenge. In this paper, we propose PILLOW, which aims to improve LoRA's performance by a discrimination-based prompting method, leveraging LLMs' In-Context Learning ability. PILLOW incorporates a matching network that selects prompts from a user-defined prompt pool, concatenates the selected prompts with the user instruction as input, and performs inference using the LoRA-fine-tuned LLMs. Trained with Reinforcement Learning, PILLOW exhibits commensurate performance on various evaluation metrics compared with typical instruction fine-tuning methods, utilizing only consumer-grade GPU resources and exhibiting a large reduction in computational costs.

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