HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要