  Large Language Models (LLMs) excel in various natural language processing
tasks but struggle with hallucination issues. Existing solutions have
considered utilizing LLMs' inherent reasoning abilities to alleviate
hallucination, such as self-correction and diverse sampling methods. However,
these methods often overtrust LLMs' initial answers due to inherent biases. The
key to alleviating this issue lies in overriding LLMs' inherent biases for
answer inspection. To this end, we propose a CounterFactual Multi-Agent Debate
(CFMAD) framework. CFMAD presets the stances of LLMs to override their inherent
biases by compelling LLMs to generate justifications for a predetermined
answer's correctness. The LLMs with different predetermined stances are engaged
with a skeptical critic for counterfactual debate on the rationality of
generated justifications. Finally, the debate process is evaluated by a
third-party judge to determine the final answer. Extensive experiments on four
datasets of three tasks demonstrate the superiority of CFMAD over existing
methods.
