General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. However, their current evaluations lack alignment with the fundamental logic of legal reasoning, the legal syllogism. This hinders trust and understanding from legal experts. To bridge this gap, we introduce LAiW, the Chinese legal LLM benchmark structured around the legal syllogism. We evaluate legal LLMs across three levels of capability, each reflecting a progressively more complex stage of legal syllogism: fundamental information retrieval, legal principles inference, and advanced legal applications, and encompassing a wide range of tasks in different legal scenarios. Our automatic evaluation reveals that LLMs, despite their ability to answer complex legal questions, lack the inherent logical processes of the legal syllogism. This limitation poses a barrier to acceptance by legal professionals. Furthermore, manual evaluation with legal experts confirms this issue and highlights the importance of pre-training on legal text to enhance the legal syllogism of LLMs. Future research may prioritize addressing this gap to unlock the full potential of LLMs in legal applications.

“本研究旨在提高中小学生作文评改的质量和效率,通过引入先进的自然语言处理模型进行作文病句检测、纠正和流畅性评分,并分别针对三个具体的任务进行了模型构建。在任务一中,提出语法错误替换方法进行数据增强,接着基于UTC模型对语病类型进行识别。在任务二中,融合了预训练的BART模型和SynGEC策略进行文本纠错,充分利用了BART的生成能力和SynGEC的语法纠错特性。任务三中,基于TextRCNN-NEZHA模型进行作文流畅性的评级,构建了一个能够综合语义信息的分类器。经评测,本文提出的方法在任务一和任务二中均位列第一,任务三位列第二,即提出的方法可以有效地识别病句类型和纠正作文中的病句,并给出合理的作文流畅性评级。”

In multidimensional dialogues, emotions serve not only as crucial mediators of emotional exchanges but also carry rich information. Therefore, accurately identifying the emotions of interlocutors and understanding the triggering factors of emotional changes are paramount. This study focuses on the tasks of multilingual dialogue emotion recognition and emotion reversal reasoning based on provocateurs, aiming to enhance the accuracy and depth of emotional understanding in dialogues. To achieve this goal, we propose a novel model, MBERT-TextRCNN-PL, designed to effectively capture emotional information of interlocutors. Additionally, we introduce XGBoost-EC (Emotion Capturer) to identify emotion provocateurs, thereby delving deeper into the causal relationships behind emotional changes. By comparing with state-of-the-art models, our approach demonstrates significant improvements in recognizing dialogue emotions and provocateurs, offering new insights and methodologies for multilingual dialogue emotion understanding and emotion reversal research.