Mitigating Biases of Large Language Models in Stance Detection with
  Calibration
Ang Li Jingqian Zhao Bin Liang Lin Gui Hui Wang Xi Zeng Kam-Fai Wong Ruifeng Xu
Large language models (LLMs) have achieved remarkable progress in many
natural language processing tasks. However, our experiment reveals that, in
stance detection tasks, LLMs may generate biased stances due to spurious
sentiment-stance correlation and preference towards certain individuals and
topics, thus harming their performance. Therefore, in this paper, we propose to
Mitigate Biases of LLMs in stance detection with Calibration (MB-Cal). In
which, a novel gated calibration network is devised to mitigate the biases on
the stance reasoning results from LLMs. Further, to make the calibration more
accurate and generalizable, we construct counterfactual augmented data to
rectify stance biases. Experimental results on in-target and zero-shot stance
detection tasks show that the proposed MB-Cal can effectively mitigate biases
of LLMs, achieving state-of-the-art results.