用多领域LoRA动态融合，让科学文本跨域通俗化更精准
Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing
- 通过动态加权多个领域LoRA，自动适应输入文本的领域特征
- 在12个领域5个数据集上显著优于现有大模型
- 适合需要跨学科科普内容生成的研究者与教育工作者
通俗化表述旨在使非专业受众理解科学信息。然而，现有研究大多局限于单一领域（如生物医学）。随着跨学科研究兴起，理解多领域知识变得日益重要。为此，我们提出Sci-LoRA，一种基于多个科学领域微调的LoRA混合模型。该模型动态生成并应用各LoRA权重，根据输入文本自动调整不同领域的影响力，无需显式提供领域标签。为平衡领域专长与跨域泛化能力，Sci-LoRA在数据和模型层面融合信息。动态融合机制提升了模型在多领域中的适应性与表现。在五个公开数据集上的十二个领域实验表明，Sci-LoRA显著优于当前最优的大语言模型，展现出灵活的跨域泛化与适应能力。
原文摘要 · Abstract (English)
Lay paraphrasing aims to make scientific information accessible to audiences without technical backgrounds. However, most existing studies focus on a single domain, such as biomedicine. With the rise of interdisciplinary research, it is increasingly necessary to comprehend knowledge spanning multiple technical fields. To address this, we propose Sci-LoRA, a model that leverages a mixture of LoRAs fine-tuned on multiple scientific domains. In particular, Sci-LoRA dynamically generates and applies weights for each LoRA, enabling it to adjust the impact of different domains based on the input text, without requiring explicit domain labels. To balance domain-specific knowledge and generalization across various domains, Sci-LoRA integrates information at both the data and model levels. This dynamic fusion enhances the adaptability and performance across various domains. Experimental results across twelve domains on five public datasets show that Sci-LoRA significantly outperforms state-of-the-art large language models and demonstrates flexible generalization and adaptability in cross-domain lay paraphrasing.
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