Synthetic Data Generation for Training Diversified Commonsense Reasoning Models

TL;DR

This paper introduces a novel two-stage method to generate a large synthetic dataset for diversified commonsense reasoning, enhancing training data quality and diversity for conversational agents.

Contribution

We propose the first synthetic dataset for diversified commonsense reasoning, addressing data scarcity and improving model diversity and quality.

Findings

Models trained on our synthetic data outperform those trained on human-crafted datasets.

Synthetic data improves both diversity and quality of generated responses.

Our approach scales across different sizes of Large Language Models.

Abstract

Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering multiple plausible alternative scenarios, reflecting the diversity in their responses. Despite the growing need to train diverse commonsense generators, the progress of this line of work has been significantly hindered by the lack of large-scale high-quality diverse commonsense training datasets. Due to the high annotation costs, existing Generative Commonsense Reasoning (GCR) datasets are created using a small number of human annotators, covering only a narrow set of commonsense scenarios. To address this training resource gap, we propose a two-stage method to create the first-ever synthetic dataset CommonSyn for diversified (GCR). The model fine-tuned on our synthetic data jointly increase both generation diversity and quality compared with vanilla models and the model fine-tuned on human-crafted dataset across different size Large Language Models (LLMs)