HELLaMA: LLaMA-based Table to Text Generation by Highlighting the
  Important Evidence
Junyi Bian Xiaolei Qin Wuhe Zou Mengzuo Huang Weidong Zhang
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.