mlsanityData to 14 Oct 2026

Are Today's LLMs Ready to Explain Well-Being Concepts?

arXiv 2508.03990 ↗First posted 6 Aug 2025cs.CL cs.AI cs.HCcited by 0

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 31 of its 49 references that are in the map. First posted on arXiv 6 Aug 2025.

Builds on

31 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024LLM Critics Help Catch LLM Bugs28 Jun

Other references · 30

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  7. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  8. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  9. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  10. 2017Constrained Policy Optimizationcited by 983
  11. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 722
  12. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
18 more
  1. 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 422
  2. 2025Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domainscited by 366
  3. 2023FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Setscited by 201
  4. 2023Can LLM-Generated Misinformation Be Detected?cited by 177
  5. 2025Checklists Are Better Than Reward Models For Aligning Language Modelscited by 128
  6. 2025Preference Leakage: A Contamination Problem in LLM-as-a-judgecited by 125
  7. 2023ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewingcited by 122
  8. 2024The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Modelscited by 93
  9. 2023Aligning Large Language Models through Synthetic Feedbackcited by 75
  10. 2023Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detectioncited by 72
  11. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  12. 2025Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systemscited by 32
  13. 2024LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devicescited by 21
  14. 2025HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaignscited by 14
  15. 2025LLM-based Semantic Augmentation for Harmful Content Detectioncited by 7
  16. 2024Structured Dialogue System for Mental Health: An LLM Chatbot Leveraging the PM+ Guidelinescited by 6
  17. 2024"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Supportcited by 5
  18. 2023TUTORING: Instruction-Grounded Conversational Agent for Language Learnerscited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Predicting Psychological Well-Being from Spontaneous Speech using LLMs
  2. 2026Analyzing LLM Reasoning to Uncover Mental Health Stigma
  3. 2026Toward Contemplative LLM: A Modular Framework for Evaluating and Enhancing LLM Alignment in Mental Health
  4. 2025Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment
  5. 2023Rethinking Large Language Models in Mental Health Applications
  6. 2025Can Finetuing LLMs on Small Human Samples Increase Heterogeneity, Alignment, and Belief-Action Coherence?
  7. 2023Towards Interpretable Mental Health Analysis with Large Language Models
  8. 2025Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications