"Large Language Models (LLMs) have demonstrated remarkable capabilities in semantic under-standing and text generation. However, when applied to downstream tasks such as Chinese Grammatical Error Correction (CGEC), they often suffer from over-correction issues, where grammatically correct parts are mistakenly altered. Moreover, some existing methods aim to address over-correction in Sequence-to-Sequence (Seq2Seq) models, they are difficult to adapt to decoder-only LLMs. To address these challenges, we propose a Chunk-based Chain ofThought (CoT) Prompting Method. Our study is structured into three key components. Initially, we identify specific types of grammatical errors in the input sentences. Following this,sentences are segmented into smaller chunks, and each chunk is analyzed to match the detected error types. Ultimately, the aggregated information guides LLMs in performing localized correction within the input sentences. The experimental results have proved the effectiveness of our method in mitigating over-correction, achieving higher F0.5 score while maintaining robust grammatical error correction performance. This method provides innovative perspectives on employing LLMs to enhance the precision and granularity of CGEC task."

"Large Language Models (LLMs) have demonstrated significant potential in interpretable translation quality estimation by providing both holistic ratings and fine-grained feedback. However,state-of-the-art methods, such as GEMBA-MQM, still suffer from an excessive number of false positives in error prediction, leading to misalignment with human annotations and reducing interpretability. To address this issue, we propose MQM-MSC, a novel training-free framework that employs a mask-driven self-correction (MSC) mechanism. The core of MSC is to use masks to highlight error spans in the initial prediction, enabling the model to re-evaluate these masked portions and verify their correctness. This approach mirrors human cognitive processes: when individuals express inconsistent judgments about the same issue at different times, it often indicates that their initial assessment was flawed. Similarly, MSC exploits contradictions between two evaluations to identify and filter false positives, thereby improving the accuracy and reliability of error annotations. Experimental results show that MQM-MSC effectively reduces false positives across four LLMs and three language pairs, consistently improving the reliability and quality of error annotations in the GEMBA-MQM approach"

多语言神经机器翻译是解决低资源神经机器翻译的有效方法,现有方法通常依靠共享词表的方式解决英语、法语以及德语相似语言之间的多语言翻译问题。缅甸语属于一种典型的低资源语言,汉语、英语以及缅甸语之间的语言结构差异性较大,为了缓解由于差异性引起的共享词表大小受限制的问题,提出一种基于多语言联合训练的汉英缅神经机器翻译方法。在Transformer框架下将丰富的汉英平行语料与汉缅、英缅的语料进行联合训练,模型训练过程中分别在编码端和解码端将汉英缅映射在同一语义空间降低汉英缅语言结构差异性对共享词表的影响,通过共享汉英语料训练参数来弥补汉缅数据缺失的问题。实验表明在一对多、多对多的翻译场景下,提出方法相比基线模型的汉-英、英-缅以及汉-缅的BLEU值有明显的提升。