Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs

2026-10-08

AI Summary

This study evaluates the performance of Jev, a specialized decision-making model, against general-purpose large language models (LLMs) on stance detection tasks. Using the VAST and ZS-CSD datasets, we systematically compare the stance identification capabilities of Jev, multiple general LLMs including GPT-5.6, and fine-tuned models across English text and Chinese dialogue scenarios. Results indicate that Jev excels in structured decision-making and English contexts, achieving performance comparable to GPT-5.6; however, it remains inferior to strong baselines in distinguishing fine-grained stances, specifically support versus opposition, within Chinese dialogues. This work delineates the applicability boundaries of specialized decision models and provides empirical evidence to guide their optimization for multilingual, complex linguistic environments.

Abstract

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.

Problem

• Stance Detection
• Decision Models
• Large Language Models
• Conversational Context

Innovation

• Stance Detection
• Decision Model
• Large Language Models
• Cross-lingual Evaluation
• Conversational Context