CESAR: Automatic Induction of Compositional Instructions for Multi-turn
  Dialogs
Taha Aksu Devamanyu Hazarika Shikib Mehri Seokhwan Kim Dilek Hakkani-Tür Yang Liu Mahdi Namazifar
Instruction-based multitasking has played a critical role in the success of
large language models (LLMs) in multi-turn dialog applications. While publicly
available LLMs have shown promising performance, when exposed to complex
instructions with multiple constraints, they lag against state-of-the-art
models like ChatGPT. In this work, we hypothesize that the availability of
large-scale complex demonstrations is crucial in bridging this gap. Focusing on
dialog applications, we propose a novel framework, CESAR, that unifies a large
number of dialog tasks in the same format and allows programmatic induction of
complex instructions without any manual effort.
  We apply CESAR on InstructDial, a benchmark for instruction-based dialog
tasks. We further enhance InstructDial with new datasets and tasks and utilize
CESAR to induce complex tasks with compositional instructions. This results in
a new benchmark called InstructDial++, which includes 63 datasets with 86 basic
tasks and 68 composite tasks. Through rigorous experiments, we demonstrate the
scalability of CESAR in providing rich instructions. Models trained on
InstructDial++ can follow compositional prompts, such as prompts that ask for
multiple stylistic constraints.