HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text
  Hybrid Question Answering
Tongxu Luo Fangyu Lei Jiahe Lei Weihao Liu Shihu He Jun Zhao Kang Liu
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.