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

Bowen Zhang, Xianghua Fu, Daijun Ding, Hu Huang, Genan Dai +3 more
4/6/2023
cs.CL

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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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Cite this paper

@article{zhang2023investigating,
  title  = {Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media},
  author = {Bowen Zhang and Xianghua Fu and Daijun Ding and Hu Huang and Genan Dai and Nan Yin and Yangyang Li and Liwen Jing},
  year   = {2023},
  eprint = {2304.03087},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.03087v2}
}

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