Self-Agreement: A Framework for Fine-tuning Language Models to Find
  Agreement among Diverse Opinions
Shiyao Ding Takayuki Ito
Finding an agreement among diverse opinions is a challenging topic in
multiagent systems. Recently, large language models (LLMs) have shown great
potential in addressing this challenge due to their remarkable capabilities in
comprehending human opinions and generating human-like text. However, they
typically rely on extensive human-annotated data. In this paper, we propose
Self-Agreement, a novel framework for fine-tuning LLMs to autonomously find
agreement using data generated by LLM itself. Specifically, our approach
employs the generative pre-trained transformer-3 (GPT-3) to generate multiple
opinions for each question in a question dataset and create several agreement
candidates among these opinions. Then, a bidirectional encoder representations
from transformers (BERT)-based model evaluates the agreement score of each
agreement candidate and selects the one with the highest agreement score. This
process yields a dataset of question-opinion-agreements, which we use to
fine-tune a pre-trained LLM for discovering agreements among diverse opinions.
Remarkably, a pre-trained LLM fine-tuned by our Self-Agreement framework
achieves comparable performance to GPT-3 with only 1/25 of its parameters,
showcasing its ability to identify agreement among various opinions without the
need for human-annotated data.