Stance detection (SD) identifies a text's position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as input. We evaluate Large Language Models (LLMs) GPT-4o, GPT-3.5, Llama-3, and Mistral, comparing their performance to the only existing work, Target-Stance Extraction (TSE), which benefits from predefined targets. Unlike TSE, OTSD removes the dependency of a predefined list, making target generation and evaluation more challenging. We also provide a metric for evaluating target quality that correlates well with human judgment. Our experiments reveal that LLMs outperform TSE in target generation when the real target is explicitly and not explicitly mentioned in the text. Likewise, for stance detection, LLMs excel in explicit cases with comparable performance in non-explicit in general.

翻译：立场检测旨在识别文本对特定目标的立场，通常标记为支持、反对或中立。本文提出开放目标立场检测这一最具现实意义的任务，其中目标既未在训练中出现，也未作为输入提供。我们评估了大型语言模型GPT-4o、GPT-3.5、Llama-3和Mistral，并将其性能与现有唯一相关研究——基于预定义目标的立场提取方法进行对比。与后者不同，开放目标立场检测消除了对预定义目标列表的依赖，使目标生成与评估更具挑战性。我们还提出了一种与人类判断高度相关的目标质量评估指标。实验表明：在文本中明确提及及未明确提及真实目标的情况下，大型语言模型在目标生成方面均优于传统立场提取方法；在立场检测任务中，大型语言模型在明确提及场景表现优异，在非明确提及场景亦展现出相当的整体性能。