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.