Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media

• URL: http://arxiv.org/abs/2304.03087v2
• Date: Thu, 17 Oct 2024 06:35:00 GMT
• Title: Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media
• Authors: Bowen Zhang, Xianghua Fu, Daijun Ding, Hu Huang, Genan Dai, Nan Yin, Yangyang Li, Liwen Jing,
• Abstract summary: Chain-of-Thought (CoT) approach not requiring backpropagation training has emerged as a promising alternative. This paper examines CoT's effectiveness in stance detection tasks, demonstrating its superior accuracy and discussing associated challenges.
• Score: 6.980802590994858
• License:
• Abstract: Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media. Traditional approaches include conventional machine learning, early deep neural networks, and pre-trained fine-tuning models. However, with the evolution of very large pre-trained language models (VLPLMs) like ChatGPT (GPT-3.5), traditional methods face deployment challenges. The parameter-free Chain-of-Thought (CoT) approach, not requiring backpropagation training, has emerged as a promising alternative. This paper examines CoT's effectiveness in stance detection tasks, demonstrating its superior accuracy and discussing associated challenges.

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