  This paper introduces Interactive Tables (iTBLS), a dataset of interactive
conversations situated in tables from scientific articles. This dataset is
designed to facilitate human-AI collaborative problem-solving through
AI-powered multi-task tabular capabilities. In contrast to prior work that
models interactions as factoid QA or procedure synthesis, iTBLS broadens the
scope of interactions to include mathematical reasoning, natural language
manipulation, and expansion of existing tables from natural language
conversation by delineating interactions into one of three tasks:
interpretation, modification, or generation. Additionally, the paper presents a
suite of baseline approaches to iTBLS, utilizing zero-shot prompting and
parameter-efficient fine-tuning for different computing situations. We also
introduce a novel multi-step approach and show how it can be leveraged in
conjunction with parameter-efficient fine-tuning to achieve the
state-of-the-art on iTBLS; outperforming standard parameter-efficient
fine-tuning by up to 15% on interpretation, 18% on modification, and 38% on
generation.
