  Answering numerical questions over hybrid contents from the given tables and
text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs)
have gained significant attention in the NLP community. With the emergence of
large language models, In-Context Learning and Chain-of-Thought prompting have
become two particularly popular research topics in this field. In this paper,
we introduce a new prompting strategy called Hybrid prompt strategy and
Retrieval of Thought for TextTableQA. Through In-Context Learning, we prompt
the model to develop the ability of retrieval thinking when dealing with hybrid
data. Our method achieves superior performance compared to the fully-supervised
SOTA on the MultiHiertt dataset in the few-shot setting.
