Integrating structured knowledge from tabular formats poses significant challenges within natural language processing (NLP), mainly when dealing with complex, semi-structured tables like those found in the FeTaQA dataset. These tables require advanced methods to interpret and generate meaningful responses accurately. Traditional approaches, such as SQL and SPARQL, often fail to fully capture the semantics of such data, especially in the presence of irregular table structures like web tables. This paper addresses these challenges by proposing a novel approach that extracts triples straightforward from tabular data and integrates it with a retrieval-augmented generation (RAG) model to enhance the accuracy, coherence, and contextual richness of responses generated by a fine-tuned GPT-3.5-turbo-0125 model. Our approach significantly outperforms existing baselines on the FeTaQA dataset, particularly excelling in Sacre-BLEU and ROUGE metrics. It effectively generates contextually accurate and detailed long-form answers from tables, showcasing its strength in complex data interpretation.

翻译：将表格格式的结构化知识整合到自然语言处理（NLP）中面临重大挑战，尤其是在处理复杂、半结构化的表格时，例如FeTaQA数据集中的表格。这些表格需要先进的方法来准确解释并生成有意义的回答。传统方法，如SQL和SPARQL，往往无法完全捕捉此类数据的语义，特别是在处理不规则表格结构（如网页表格）时。本文通过提出一种新颖方法来解决这些挑战，该方法直接从表格数据中提取三元组，并将其与检索增强生成（RAG）模型相结合，以提升经过微调的GPT-3.5-turbo-0125模型生成回答的准确性、连贯性和上下文丰富性。我们的方法在FeTaQA数据集上显著优于现有基线，尤其在Sacre-BLEU和ROUGE指标上表现突出。它能够有效地从表格中生成上下文准确且详细的长篇答案，展示了其在复杂数据解释方面的优势。