HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation

• URL: http://arxiv.org/abs/2311.08896v2
• Date: Sat, 27 Apr 2024 12:51:11 GMT
• Title: HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation
• Authors: Junyi Bian, Xiaolei Qin, Wuhe Zou, Mengzuo Huang, Congyi Luo, Ke Zhang, Weidong Zhang,
• Abstract summary: We conduct parameter-efficient fine-tuning on the LLaMA2 model. Our approach involves injecting reasoning information into the input by emphasizing table-specific row data. On both the FetaQA and QTSumm datasets, our approach achieved state-of-the-art results.
• Score: 7.69801337810352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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