Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements

arXiv:2402.10614v1 Announce Type: cross
Abstract: 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 …

arxiv cs.ai cs.cl cs.lg diverse generate llms people speak type via