  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
sentiment-stance spurious correlations 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). To be specific, a novel calibration network is devised to
calibrate potential bias in the stance prediction of LLMs. Further, to address
the challenge of effectively learning bias representations and the difficulty
in the generalizability of debiasing, we construct counterfactual augmented
data. This approach enhances the calibration network, facilitating the
debiasing and out-of-domain generalization. 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.
