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

Key points

• Jev, a specialized decision model designed for structured decision-making, offers an alternative to general-purpose large language models (LLMs).1
• 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.1
• 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.1
• These findings highlight both the potential and limitations of Jev for stance detection.1

Sources (1)

• [1]Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 01:05 PM

  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).

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