Enhancing Table QA Accuracy with Semantic Extraction

View a PDF of the paper titled Information in Triples for LLMs: Enhancing Desk QA Accuracy with Semantic Extraction, by Hossein Sholehrasa and three different authors

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Summary:Integrating structured data from tabular codecs poses important challenges inside pure language processing (NLP), primarily when coping with advanced, semi-structured tables like these discovered within the FeTaQA dataset. These tables require superior strategies to interpret and generate significant responses precisely. Conventional approaches, resembling SQL and SPARQL, usually fail to totally seize the semantics of such information, particularly within the presence of irregular desk constructions like net tables. This paper addresses these challenges by proposing a novel strategy that extracts triples simple from tabular information and integrates it with a retrieval-augmented technology (RAG) mannequin to boost the accuracy, coherence, and contextual richness of responses generated by a fine-tuned GPT-3.5-turbo-0125 mannequin. Our strategy considerably outperforms present baselines on the FeTaQA dataset, notably excelling in Sacre-BLEU and ROUGE metrics. It successfully generates contextually correct and detailed long-form solutions from tables, showcasing its power in advanced information interpretation.