发表机构
Dalian Maritime University; Dalian University of Technology（大连海事大学; 大连理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估专用决策模型Jev在立场检测任务上的表现，对比通用LLMs及微调模型，发现Jev在英文数据集VAST上具竞争力但在中文对话数据集ZS-CSD上存在局限，凸显其潜力与不足。
AI 中文摘要
立场检测需要识别作者对给定目标的态度，有时需基于对话上下文。Jev是一种专为结构化决策设计的专用决策模型，可作为通用大语言模型（LLMs）的替代方案。本研究在两个立场检测数据集（英文文本VAST、中文对话ZS-CSD）上评估Jev，将其与4个通用LLMs及2个微调模型对比。结果显示，Jev在VAST上表现具竞争力，与GPT-5.6相当且优于其他通用LLMs；但在ZS-CSD上落后于更强的LLMs，尤其在区分支持与反对立场时。进一步分析表明，该局限可能与对回复关系和立场方向的理解有关，而非仅对话长度。这些发现凸显了Jev用于立场检测的潜力与局限。
英文摘要
Stance detection requires identifying an author's attitude toward a given target, sometimes based on conversational context. Jev, a specialized decision model designed for structured decision-making, offers an alternative to general-purpose large language models (LLMs). In this work, we evaluate Jev on two stance detection datasets, VAST (English texts) and ZS-CSD (Chinese conversations), comparing it with four general-purpose LLMs and two fine-tuned models. Results show that Jev achieves competitive performance on VAST, matching GPT-5.6 and outperforming the other general-purpose LLMs. However, it falls behind stronger LLMs on ZS-CSD, particularly in distinguishing favor from against. Further analysis suggests that this limitation may be related to understanding reply relationships and stance direction rather than conversation length alone. These findings highlight both the potential and limitations of Jev for stance detection.
Comments8 pages, 1 figure, 5 tables
