发表机构
School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Emo-Jev，一种无需训练的框架，通过分解原子判断或聚合多条判断路径的概率决策，在情感分类任务上以较低延迟和成本接近领先LLM的性能。
AI 中文摘要
Jev为语言理解提供了一种替代接口：给定输入和预定义问题，它返回概率决策而非自由形式的响应。这种接口能否支持针对领先LLM的文本分类的有效推理仍是一个开放问题。我们引入了Emo-Jev，一个无需训练且具有两种互补实现的框架。Emo-Jev-D将分类分解为任务特定的原子判断，并将其概率组合成最终预测。Emo-Jev-SC从互补视角构建多条判断路径，并将其预测聚合为共识决策。我们在涵盖情感分析、情绪识别、讽刺检测和幽默检测的八个数据集上评估了Emo-Jev，与直接Jev分类和五种SoTA LLM在输入/输出和思维链推理下进行比较。标准Jev实现了62.93%的平均宏F1，而最强LLM基线为67.28%，且具有更低的观测延迟和通常更低的成本。
英文摘要
Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than free-form responses. Whether this interface can support effective reasoning for text classification against leading LLMs remains an open questions. We introduce Emo-Jev, a training-free framework with two complementary implementations. Emo-Jev-D decomposes classification into task-specific atomic judgments and composes their probabilities into a final prediction. Emo-Jev-SC constructs multiple judgment paths from complementary perspectives and aggregates their predictions into a consensus decision. We evaluate Emo-Jev on eight datasets spanning sentiment analysis, emotion recognition, sarcasm detection and humor detection, comparing against direct Jev classification and five SoTA LLMs under input/output and chain-of-thought reasoning. Standard Jev achieves 62.93\% average macro-F1 versus 67.28\% for the strongest LLM baseline, with lower observed latency and generally lower cost.
