Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate
  Controllable Controversial Statements
Ming Li Jiuhai Chen Lichang Chen Tianyi Zhou
Making LLMs speak for different, especially minority groups of people, and
generate statements supporting their diverse or even controversial perspectives
is critical to creating an inclusive environment. However, existing LLMs lack
sufficient controllability to the stance of their generated content, which
often contains inconsistent, neutral, or biased statements. In this paper, we
improve the controllability of LLMs in generating statements supporting an
argument the user defined in the prompt. We find that multi-round debates
between two LLMs with opposite stances generate higher-quality and more salient
statements for each, which are important training data to improve the
controllability of LLMs. Motivated by this, we develop a novel debate & tuning
("DEBATunE") pipeline finetuning LLMs to generate the statements obtained via
debate. To examine DEBATunE, we curate the largest dataset of debate topics so
far, which covers 710 controversial topics and corresponding arguments for each
topic. Evaluations by the GPT-4 judge with a novel controversy controllability
metric show that LLMs' capability of expressing diverse perspectives is
significantly improved by DEBATunE. Moreover, such controllability can be
generalized to unseen topics, generating high-quality statements supporting
controversial arguments. Our codes, models, and data will be released at
https://github.com/tianyi-lab/DEBATunE.