The research presents a novel approach to enhance the understanding of tabular data extracted from PDFs for efficient summarization by language models. The approach involves storing PDFs in the retrieval database, extracting the tabular content, and contextually enriching it by appending headers with corresponding values. A fine-tuned version of the Llama-2-chat language model is used for summarization within the Retrieval-Augmented Generation architecture. The enriched data is then fed back into the retrieval database. This methodology aims to significantly improve the precision of complex table queries, offering a solution to a longstanding challenge in information retrieval.
Publication date: 5 Jan 2024
Project Page: Not provided
Paper: https://arxiv.org/pdf/2401.02333
