Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successfully transferred to non-high-resource languages due to resource constraints, which struggles with multilingual reasoning tasks. To this end, we propose Structured-of-Thought (SoT), a training-free method that improves the performance on multilingual reasoning through a multi-step transformation: Language Thinking Transformation and Structured Knowledge Transformation. The SoT method converts language-specific semantic information into language-agnostic structured representations, enabling the models to understand the query in different languages more sophisticated. Besides, SoT effectively guides LLMs toward more concentrated reasoning to maintain consistent underlying reasoning pathways when handling cross-lingual variations in expression. Experimental results demonstrate that SoT outperforms several strong baselines on multiple multilingual reasoning benchmarks when adapting to various backbones of LLMs. It can also be integrated with other training-free strategies for further improvements. Our code is available at https://github.com/Cherry-qwq/SoT.

“减小图文信息间的语义鸿沟,促进跨模态信息的对齐与融合一直是解决跨模态图文检索问题的关键。但现有的双流模型因为训练时图像编码器与文本编码器是分开的,导致图文特征的对齐与融合较难。因此,本文提出图像映射文本特征(MITF)网络将不同模态(图像和文本)的信息映射到单一模态(文本),进一步增强跨模态语义的融合和对齐,提高图文检索的性能。具体地,在冻结预训练的中文视觉语言模型Chinese-CLIP参数的情况下,训练一个MITF网络将图像映射为伪语言标记,在此基础上引入提示词自动学习机制提升模型对于伪语言标记的理解能力。同时,在检索时构建Faiss索引提高检索速度。在三个开源数据集的实验结果表明所提方法相比原始Chinese-CLIP模型检索时的Mean Recall指标平均提高了3.7%,检索速度提高了约4倍。同时,图文特征可视化结果进一步表明所提方法提高了图像特征与文本特征的对齐程度。”

“k-最近邻机器翻译(kNN-MT)是近年来神经机器翻译领域的一个重要研究方向。此类方法可以在不更新机器翻译模型的情况下提高翻译质量,但训练数据中高低频单词的数量不均衡限制了模型效果,且固定的k值无法对处于不同密度分布的数据都产生良好的翻译结果。为此本文提出了一种创新的kNN-MT方法,引入确定性因子(CF)来降低数据不均衡对模型效果的影响,并根据测试点周边数据密度动态选择k值。在多领域德-英翻译数据集上,相比基线实验,本方法在四个领域上翻译效果均有提升,其中三个领域上提升超过1个BLEU,有效提高了神经机器翻译模型的翻译质量。”