  A myriad of different Large Language Models (LLMs) face a common challenge in
contextually analyzing table question-answering tasks. These challenges are
engendered from (1) finite context windows for large tables, (2) multi-faceted
discrepancies amongst tokenization patterns against cell boundaries, and (3)
various limitations stemming from data confidentiality in the process of using
external models such as gpt-3.5-turbo. We propose a cooperative game dubbed
"HiddenTables" as a potential resolution to this challenge. In essence,
"HiddenTables" is played between the code-generating LLM "Solver" and the
"Oracle" which evaluates the ability of the LLM agents to solve Table QA tasks.
This game is based on natural language schemas and importantly, ensures the
security of the underlying data. We provide evidential experiments on a diverse
set of tables that demonstrate an LLM's collective inability to generalize and
perform on complex queries, handle compositional dependencies, and align
natural language to programmatic commands when concrete table schemas are
provided. Unlike encoder-based models, we have pushed the boundaries of
"HiddenTables" to not be limited by the number of rows - therefore we exhibit
improved efficiency in prompt and completion tokens. Our infrastructure has
spawned a new dataset "PyQTax" that spans across 116,671 question-table-answer
triplets and provides additional fine-grained breakdowns & labels for varying
question taxonomies. Therefore, in tandem with our academic contributions
regarding LLMs' deficiency in TableQA tasks, "HiddenTables" is a tactile
manifestation of how LLMs can interact with massive datasets while ensuring
data security and minimizing generation costs.
