Persona Knowledge-Aligned Prompt Tuning Method for Online Debate
Chunkit Chan, Cheng Jiayang, Xin Liu, Yauwai Yim, Yuxin Jiang, Zheye Deng, Haoran Li, Yangqiu Song, Ginny Y. Wong, Simon See
TL;DR
This work proposes a persona knowledge-aligned framework for argument quality assessment tasks from the audience side and is the first work that leverages the emergence of ChatGPT and injects such audience personae knowledge into smaller language models via prompt tuning.
Abstract
Debate is the process of exchanging viewpoints or convincing others on a particular issue. Recent research has provided empirical evidence that the persuasiveness of an argument is determined not only by language usage but also by communicator characteristics. Researchers have paid much attention to aspects of languages, such as linguistic features and discourse structures, but combining argument persuasiveness and impact with the social personae of the audience has not been explored due to the difficulty and complexity. We have observed the impressive simulation and personification capability of ChatGPT, indicating a giant pre-trained language model may function as an individual to provide personae and exert unique influences based on diverse background knowledge. Therefore, we propose a persona knowledge-aligned framework for argument quality assessment tasks from the audience side. This is the first work that leverages the emergence of ChatGPT and injects such audience personae knowledge into smaller language models via prompt tuning. The performance of our pipeline demonstrates significant and consistent improvement compared to competitive architectures.