Xiaokang Zhang

Daniel Zhang-Li, Nianyi Lin, Jifan Yu et al.

Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite acknowledging the significance of digit order in arithmetic computation, current methodologies predominantly rely on sequential, step-by-step approaches for teaching LLMs arithmetic, resulting in a conclusion where obtaining better performance involves fine-grained step-by-step. Diverging from this conventional path, our work introduces a novel strategy that not only reevaluates the digit order by prioritizing output from the least significant digit but also incorporates a step-by-step methodology to substantially reduce complexity. We have developed and applied this method in a comprehensive set of experiments. Compared to the previous state-of-the-art (SOTA) method, our findings reveal an overall improvement of in accuracy while requiring only a third of the tokens typically used during training. For the purpose of facilitating replication and further research, we have made our code and dataset publicly available at \url{https://anonymous.4open.science/r/RAIT-9FB7/}.

Maofu Liu, Jiahui Liu, Xiaokang Zhang

Remote sensing image captioning aims to generate semantically accurate descriptions that are closely linked to the visual features of remote sensing images. Existing approaches typically emphasize fine-grained extraction of visual features and capturing global information. However, they often overlook the complementary role of textual information in enhancing visual semantics and face challenges in precisely locating objects that are most relevant to the image context. To address these challenges, this paper presents a semantic-spatial feature fusion with dynamic graph refinement (SFDR) method, which integrates the semantic-spatial feature fusion (SSFF) and dynamic graph feature refinement (DGFR) modules. The SSFF module utilizes a multi-level feature representation strategy by leveraging pre-trained CLIP features, grid features, and ROI features to integrate rich semantic and spatial information. In the DGFR module, a graph attention network captures the relationships between feature nodes, while a dynamic weighting mechanism prioritizes objects that are most relevant to the current scene and suppresses less significant ones. Therefore, the proposed SFDR method significantly enhances the quality of the generated descriptions. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method. The source code will be available at https://github.com/zxk688}{https://github.com/zxk688.