Are Today's LLMs Ready to Explain Well-Being Concepts?
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.
68955a4548b0ae5ca2710d24
Bohan Jiang
68955a4548b0ae5ca2710d25
Dawei Li
claimed_verified
2025-08-08T16:19:55.185Z
6894453303e47d990aade1c6
/avatars/92a9369537348f317be96dea030e90f9.svg
Bill Avan
user
BillAvan
68955a4548b0ae5ca2710d26
Zhen Tan
68955a4548b0ae5ca2710d27
Chengshuai Zhao
claimed_verified
2025-08-11T06:49:55.180Z
65b2fae679954e21ac426aec
https://cdn-avatars.huggingface.co/v1/production/uploads/65b2fae679954e21ac426aec/LybSb_awygRTQinm1npUq.jpeg
Chengshuai Zhao
user
chengshuaizhao
68955a4548b0ae5ca2710d28
Huan Liu
Are Today's LLMs Ready to Explain Well-Being Concepts?
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.
LLMs can be fine-tuned to generate high-quality, audience-tailored explanations of well-being concepts using Supervised Fine-Tuning and Direct Preference Optimization.
Large Language Models LLMs Supervised Fine-Tuning Direct Preference Optimization LLM-as-a-judge explanation quality fine-tuning preference-based learning