Year: 2026
Type: conference-paper
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 CommonSyn, the first-ever synthetic dataset for diversified GCR.Large Language Models (LLMs) fine-tuned on CommonSyn show simultaneous improvements in both generation diversity and quality compared with vanilla models and models fine-tuned on manually annotated datasets.1
Authors: Tianhui Zhang, Bei Peng, Danushka Bollegala
Institutions: University of Sheffield, University of Liverpool
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166799156