Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection
Maxwell Weinzierl, Sanda M. Harabagiu · 2024
Stance detection enables the inference of attitudes from human communications.Automatic stance identification was mostly cast as a classification problem.However, stance decisions involve complex judgments, which can be nowadays generated by prompting Large Language Models (LLMs).In this paper we present a new method for stance identification which (1) relies on a new prompting framework, called Tree-of-Counterfactual prompting; (2) operates not only on textual communications, but also on images; (3) allows more than one stance object type; and (4) requires no examples of stance attribution, thus it is a "Tabula Rasa" Zero-Shot Stance Detection (TR-ZSSD) method.Our experiments indicate surprisingly promising results, outperforming fine-tuned stance detection systems.