Runze Cai

Kexue Fu, Jiaye Leng, Yawen Zhang et al.

Balancing scientific exposition and narrative engagement is a central challenge in science communication. To examine how to achieve balance, we conducted a formative study with four science communicators and a literature review of science communication practices, focusing on their workflows and strategies. These insights revealed how creators iteratively shift between exposition and engagement but often lack structured support. Building on this, we developed SpatialBalancing, a co-writing system that connects human spatial reasoning with the linguistic intelligence of large language models. The system visualizes revision trade-offs in a dual-axis space, where users select strategy-based labels to generate, compare, and refine versions during the revision process. This spatial externalization transforms revision into spatial navigation, enabling intentional iterations that balance scientific rigor with narrative appeal. In a within-subjects study (N=16), SpatialBalancing enhanced metacognitive reflection, flexibility, and creative exploration, demonstrating how coupling spatial reasoning with linguistic generation fosters monitoring in iterative science communication writing.

Luping Wang, Sheng Chen, Linnan Jiang et al.

The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. These large models require substantial computational resources and GPU memory to operate. When adapting large models to specific downstream tasks, their massive parameter scale poses a significant challenge in fine-tuning on hardware platforms with limited computational power and GPU memory. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) offers a practical solution by efficiently adjusting the parameters of large pre-trained models to suit various downstream tasks. Specifically, PEFT adjusts the parameters of pre-trained large models to adapt to specific tasks or domains, minimizing the introduction of additional parameters and the computational resources required. This review mainly introduces the preliminary knowledge of PEFT, the core ideas and principles of various PEFT algorithms, the applications of PEFT, and potential future research directions. By reading this review, we believe that interested parties can quickly grasp the PEFT methodology, thereby accelerating its development and innovation.