Are Today's LLMs Ready to Explain Well-Being Concepts?
Authors: name: Bohan Jiang, name: Dawei Li, name: Zhen Tan, name: Chengshuai Zhao, name: Huan Liu

Well-being encompasses mental, physical, and social dimensions essential to
personal growth and informed life decisions. As individuals increasingly
consult Large Language Models (LLMs) to understand well-being, a key challenge
emerges: Can LLMs generate explanations that are not only accurate but also
tailored to diverse audiences? High-quality explanations require both factual
correctness and the ability to meet the expectations of users with varying
expertise. In this work, we construct a large-scale dataset comprising 43,880
explanations of 2,194 well-being concepts, generated by ten diverse LLMs. We
introduce a principle-guided LLM-as-a-judge evaluation framework, employing
dual judges to assess explanation quality. Furthermore, we show that
fine-tuning an open-source LLM using Supervised Fine-Tuning (SFT) and Direct
Preference Optimization (DPO) can significantly enhance the quality of
generated explanations. Our results reveal: (1) The proposed LLM judges align
well with human evaluations; (2) explanation quality varies significantly
across models, audiences, and categories; and (3) DPO- and SFT-finetuned models
outperform their larger counterparts, demonstrating the effectiveness of
preference-based learning for specialized explanation tasks.