We present a novel end-to-end personality-based synthetic dialogue data generation pipeline, specifically designed to elicit responses from large language models via prompting. We design the prompts to generate more human-like dialogues considering real-world scenarios when users engage with chatbots. We introduce PSYDIAL, the first Korean dialogue dataset focused on personality-based dialogues, curated using our proposed pipeline. Notably, we focus on the Extraversion dimension of the Big Five personality model in our research. Experimental results indicate that while pre-trained models and those fine-tuned with a chit-chat dataset struggle to generate responses reflecting personality, models trained with PSYDIAL show significant improvements. The versatility of our pipeline extends beyond dialogue tasks, offering potential for other non-dialogue related applications. This research opens doors for more nuanced, personality-driven conversational AI in Korean and potentially other languages.

In few-shot text classification, self-training is a popular tool in semi-supervised learning (SSL). It relies on pseudo-labels to expand data, which has demonstrated success. However, these pseudo-labels contain potential noise and provoke a risk of underfitting the decision boundary. While the pseudo-labeled data can indeed be noisy, fully acquiring this flawed data can result in the accumulation of further noise and eventually impacting the model performance. Consequently, self-training presents a challenge: mitigating the accumulation of noise in the pseudo-labels. Confronting this challenge, we introduce superficial learning, inspired by pedagogy’s focus on essential knowledge. Superficial learning in pedagogy is a learning scheme that only learns the material ‘at some extent’, not fully understanding the material. This approach is usually avoided in education but counter-intuitively in our context, we employ superficial learning to acquire only the necessary context from noisy data, effectively avoiding the noise. This concept serves as the foundation for SuperST, our self-training framework. SuperST applies superficial learning to the noisy data and fine-tuning to the less noisy data, creating an efficient learning cycle that prevents overfitting to the noise and spans the decision boundary effectively. Notably, SuperST improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines. We substantiate our claim through empirical experiments and decision boundary analysis.