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.
@article{arxiv.2311.08896,
title = {HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation},
author = {Junyi Bian and Xiaolei Qin and Wuhe Zou and Mengzuo Huang and Congyi Luo and Ke Zhang and Weidong Zhang},
journal= {arXiv preprint arXiv:2311.08896},
year = {2024}
}