HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation
This addresses the challenge of generating accurate and interpretable text from tables for users in data analysis and NLP applications, representing an incremental improvement over existing fine-tuning methods.
The paper tackled the problem of table-to-text generation by fine-tuning the LLaMA2 model with a method that highlights relevant row evidence, achieving state-of-the-art results on the FetaQA and QTSumm datasets.
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.