从表格中提取三元组增强大模型问答准确率
Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction
- 从复杂表格直接提取语义三元组，融入检索生成模型
- 在FeTaQA数据集上显著提升Sacre-BLEU与ROUGE指标
- 适合需要精准表格问答的科研与产品场景
将结构化表格知识整合到自然语言处理中面临重大挑战，尤其在处理如FeTaQA数据集中的复杂半结构化表格时。这类表格需先进方法解析以生成准确响应。传统方法如SQL和SPARQL难以充分捕捉此类数据的语义，尤其是在存在异常表格结构（如网页表格）的情况下。本文提出一种新方法，直接从表格数据中提取三元组，并将其与检索增强生成（RAG）模型结合，用于提升微调后的GPT-3.5-turbo-0125模型生成答案的准确性、连贯性与上下文丰富性。该方法在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，能有效生成上下文准确且详尽的长文本回答，展现出在复杂数据理解方面的优势。
原文摘要 · Abstract (English)
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.
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