Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Maxwell Weinzierl, Sanda Harabagiu
Institution: The University of Texas at Dallas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402667129