Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing

Abstract: 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 LLMs and demonstrates flexible generalization and adaptability in cross-domain lay paraphrasing.

Sci-LoRA: Enhancing Cross-Domain Lay Paraphrasing

The paper titled "Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing" introduces a novel approach to improving the accessibility of scientific information across various domains. Unlike previous efforts that primarily focus on domain-specific lay paraphrasing, Sci-LoRA aims to dynamically integrate knowledge from multiple scientific fields. This integration is achieved through Low-Rank Adaptation (LoRA), which fine-tunes large language models to adaptively respond to the diverse demands of interdisciplinary content without requiring explicit domain labels.

Model Architecture and Methodology

The core innovation of Sci-LoRA lies in its mixture of LoRAs, each fine-tuned on data from different scientific domains. The model dynamically assigns weights to these adapters, allowing for effective cross-domain knowledge utilization. A specially designed adapter weight generator calculates these weights based on domain relevance, informed by contrastive-learning-enhanced text encoders. The text encoder facilitates the fine-tuning process by differentiating between domain-specific features, ensuring that Sci-LoRA can balance domain-specific precision with cross-domain applicability.

Furthermore, Sci-LoRA employs a dynamic fusion strategy, integrating specialized domain knowledge with broader, generalized information from multi-domain data. This fusion enhances the model’s ability to generalize while preserving the accuracy of domain-specific content.

Experimental Validation

Experiments conducted across twelve scientific domains using five public datasets demonstrate that Sci-LoRA significantly surpasses state-of-the-art models in lay paraphrasing tasks. This is evidenced through superior performance across various evaluation metrics, including BERTScore, ROUGE, METEOR, and others. The results underscore Sci-LoRA's flexible adaptability and capability in generating layman-friendly paraphrased content without loss of scientific accuracy.

Practical Implications and Future Directions

Sci-LoRA offers substantial practical implications for fields requiring the dissemination of technical knowledge to non-experts. Its adaptability across domains minimizes the need for frequent retraining, making it an efficient tool for interdisciplinary research communication. Furthermore, the methodology underpinning Sci-LoRA—particularly its mixture and dynamic fusion techniques—can inform future development in AI models that require nuanced cross-domain integration.

Theoretically, Sci-LoRA opens pathways for exploring LoRA's potential beyond specialized scientific domains. Future research may focus on scaling Sci-LoRA to accommodate a wider array of disciplines, investigating few-shot learning for emergent fields, and assessing its integration with alternative model architectures. These endeavors will not only enhance the efficacy of lay paraphrasing but also contribute to the broader aim of democratizing access to scientific knowledge.

Overall, Sci-LoRA represents a valuable advancement in leveraging parameter-efficient fine-tuning strategies to address the challenges of interdisciplinary lay paraphrasing, with substantial promise for both academic knowledge dissemination and AI development.