Large language models (LLMs) can adapt to new tasks through in-context
learning (ICL) based on a few examples presented in dialogue history without
any model parameter update. Despite such convenience, the performance of ICL
heavily depends on the quality of the in-context examples presented, which
makes the in-context example selection approach a critical choice. This paper
proposes a novel Bayesian in-Context example Selection method (ByCS) for ICL.
Extending the inference probability conditioned on in-context examples based on
Bayes' theorem, ByCS focuses on the inverse inference conditioned on test
input. Following the assumption that accurate inverse inference probability
(likelihood) will result in accurate inference probability (posterior),
in-context examples are selected based on their inverse inference results.
Diverse and extensive cross-tasking and cross-modality experiments are
performed with speech, text, and image examples. Experimental results show the
efficacy and robustness of our ByCS method on various models, tasks and
modalities.

基于大语言模型，提出了一种用于上下文学习的贝叶斯上下文示例选择方法（ByCS），通过基于贝叶斯定理的上下文示例条件概率推理，选择准确的倒推结果来提高性能，并通过多样性和广泛的跨任务和跨模态实验证明了该方法的有效性和鲁棒性。