让提示词和参数一起优化，提升大模型表现
Prompt and Parameter Co-Optimization for Large Language Models
- 用两个神经网络同时生成提示词和参数，共享底层编码
- 在多个基准上表现优于传统方法，提升稳定可靠
- 适合需要高效微调大模型的科研与工程人员
提示词优化与微调是提升大语言模型性能的两大主流方法，分别通过显式的自然语言和隐式的参数更新增强模型能力。然而，以往研究多将二者孤立处理，未充分挖掘其协同潜力。为此，本文提出MetaTuner框架，首次实现提示词优化与微调的联合训练。具体而言，引入两个神经网络分别生成提示词和参数，共享一个公共底座编码层以促进知识迁移，并在最终监督信号指导下，共同优化提示词与参数的最佳组合。针对提示学习的离散优化与微调的连续参数空间差异，设计了监督正则化损失以有效训练。在多个基准上的大量实验表明，该方法持续优于现有基线。
原文摘要 · Abstract (English)
Prompt optimization and fine-tuning are two major approaches to improve the performance of Large Language Models (LLMs). They enhance the capabilities of LLMs from complementary perspectives: the former through explicit natural language, and the latter through implicit parameter updates. However, prior work has typically studied them in isolation, leaving their synergistic potential largely underexplored. To bridge this gap, in this paper, we introduce MetaTuner, a novel framework that jointly integrates prompt optimization and fine-tuning for LLM training. Specifically, we introduce two neural networks to generate prompts and parameters, respectively, while allowing them to share a common bottom encoding layer to enable knowledge sharing. By the guidance of the final supervised signals, our framework is optimized to discover the optimal combinations between the prompts and parameters. Given that prompt learning involves discrete optimization while fine-tuning operates in a continuous parameter space, we design a supervised regularization loss to train our framework effectively. Extensive experiments across diverse benchmarks show that our method consistently outperforms the baselines.
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