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.

翻译：大模型在各个领域取得了显著进展，特别是在文本生成相关任务中。在表格到文本领域，当前许多基于大语言模型（LLM）的方法依赖于修改提示词来调用公共API，这带来了潜在成本与信息泄露风险。随着开源大模型的出现，对LLM进行微调成为可行方案。本研究对LLaMA2模型进行了参数高效微调。区别于以往基于微调的表格到文本方法，我们通过强调表格特定行数据，将推理信息注入输入中。我们的模型包含两个模块：1）表格推理器，用于识别相关行证据；2）表格总结器，基于高亮表格生成句子。为支持此过程，我们提出一种搜索策略来构建训练表格推理器所需的推理标签。在FetaQA和QTSumm数据集上，我们的方法取得了最先进的结果。此外，我们观察到高亮输入表格能显著提升模型性能并提供有价值的可解释性。