Bridging the Gap: Deciphering Tabular Data Using Large Language Model
This work addresses the challenge of table-based question answering for NLP researchers, but it is incremental as it builds on existing large language model approaches.
The paper tackles the problem of improving large language models' understanding of tabular data for question answering by developing a serialization module and a corrective mechanism, resulting in a method that trails SOTA by 11.7% overall but surpasses it by 1.2% on specific datasets.
In the realm of natural language processing, the understanding of tabular data has perpetually stood as a focal point of scholarly inquiry. The emergence of expansive language models, exemplified by the likes of ChatGPT, has ushered in a wave of endeavors wherein researchers aim to harness these models for tasks related to table-based question answering. Central to our investigative pursuits is the elucidation of methodologies that amplify the aptitude of such large language models in discerning both the structural intricacies and inherent content of tables, ultimately facilitating their capacity to provide informed responses to pertinent queries. To this end, we have architected a distinctive module dedicated to the serialization of tables for seamless integration with expansive language models. Additionally, we've instituted a corrective mechanism within the model to rectify potential inaccuracies. Experimental results indicate that, although our proposed method trails the SOTA by approximately 11.7% in overall metrics, it surpasses the SOTA by about 1.2% in tests on specific datasets. This research marks the first application of large language models to table-based question answering tasks, enhancing the model's comprehension of both table structures and content.