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Tables stored in databases and tables which are present in web pages andarticles account for a large part of semi-structured data that is available onthe internet. It then becomes pertinent to develop a modeling approach withlarge language models (LLMs) that can be used to solve diverse table tasks suchas semantic parsing, question answering as well as classification problems.Traditionally, there existed separate models specialized for each taskindividually. It raises the question of how far can we go to build a unifiedmodel that works well on some table tasks without significant degradation onothers. To that end, we attempt at creating a shared modeling approach in thepretraining stage with encoder-decoder style LLMs that can cater to diversetasks. We evaluate our approach that continually pretrains and finetunesdifferent model families of T5 with data from tables and surrounding context,on these downstream tasks at different model scales. Through multiple ablationstudies, we observe that our pretraining with self-supervised objectives cansignificantly boost the performance of the models on these tasks. As an exampleof one improvement, we observe that the instruction finetuned public modelswhich come specialized on text question answering (QA) and have been trained ontable data still have room for improvement when it comes to table specific QA.Our work is the first attempt at studying the advantages of a unified approachto table specific pretraining when scaled from 770M to 11B sequence to sequencemodels while also comparing the instruction finetuned variants of the models.

Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks