  Large models have demonstrated significant progress across various domains,
particularly in tasks related to text generation. In the domain of Table to
Text, many Large Language Model (LLM)-based methods currently resort to
modifying prompts to invoke public APIs, incurring potential costs and
information leaks. With the advent of open-source large models, fine-tuning
LLMs has become feasible. In this study, we conducted parameter-efficient
fine-tuning on the LLaMA2 model. Distinguishing itself from previous
fine-tuning-based table-to-text methods, our approach involves injecting
reasoning information into the input by emphasizing table-specific row data.
Our model consists of two modules: 1) a table reasoner that identifies relevant
row evidence, and 2) a table summarizer that generates sentences based on the
highlighted table. To facilitate this, we propose a search strategy to
construct reasoning labels for training the table reasoner. On both the FetaQA
and QTSumm datasets, our approach achieved state-of-the-art results.
Additionally, we observed that highlighting input tables significantly enhances
the model's performance and provides valuable interpretability.
