Testing the limits of unified seq2seq LLM pretraining on diverse table data tasks

Tables stored in databases and tables which are present in web pages and articles account for a large part of semi-structured data that is available on the internet. It motivates the need to develop a modeling approach with large language models (LLMs) which can be used to solve diverse table tasks such as semantic parsing, question answering as well as classification problems. Traditionally, there existed separate sequence to sequence models specialized for each table task individually. It raises the question of how far can we go to build a unified model that works well on some table tasks without significant degradation on others. To that end, we attempt at creating a shared modeling approach in the pretraining stage with encoder-decoder style LLMs that can cater to diverse tasks. We evaluate our approach that continually pretrains and finetunes different model families of T5 with data from tables and surrounding context, on these downstream tasks at different model scales. Through multiple ablation studies, we observe that our pretraining with self-supervised objectives can significantly boost the performance of the models on these tasks. Our work is the first attempt at studying the advantages of a unified approach to table specific pretraining when scaled from 770M to 11B sequence to sequence models while also comparing the instruction finetuned variants of the model.