  Table understanding capability of Large Language Models (LLMs) has been
extensively studied through the task of question-answering (QA) over tables.
Typically, only a small part of the whole table is relevant to derive the
answer for a given question. The irrelevant parts act as noise and are
distracting information, resulting in sub-optimal performance due to the
vulnerability of LLMs to noise. To mitigate this, we propose CABINET (Content
RelevAnce-Based NoIse ReductioN for TablE QuesTion-Answering) - a framework to
enable LLMs to focus on relevant tabular data by suppressing extraneous
information. CABINET comprises an Unsupervised Relevance Scorer (URS), trained
differentially with the QA LLM, that weighs the table content based on its
relevance to the input question before feeding it to the question-answering LLM
(QA LLM). To further aid the relevance scorer, CABINET employs a weakly
supervised module that generates a parsing statement describing the criteria of
rows and columns relevant to the question and highlights the content of
corresponding table cells. CABINET significantly outperforms various tabular
LLM baselines, as well as GPT3-based in-context learning methods, is more
robust to noise, maintains outperformance on tables of varying sizes, and
establishes new SoTA performance on WikiTQ, FeTaQA, and WikiSQL datasets. We
release our code and datasets at https://github.com/Sohanpatnaik106/CABINET_QA.
