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

• URL: http://arxiv.org/abs/2508.03990v1
• Date: Wed, 06 Aug 2025 00:45:02 GMT
• Title: Are Today's LLMs Ready to Explain Well-Being Concepts?
• Authors: Bohan Jiang, Dawei Li, Zhen Tan, Chengshuai Zhao, Huan Liu,
• Abstract summary: We construct a large-scale dataset comprising 43,880 explanations of 2,194 well-being concepts.<n>We introduce a principle-guided LLM-as-a-judge evaluation framework, employing dual judges to assess explanation quality.<n>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.
• Score: 17.02052397388858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models [50.16340812031201]
  We show that large language models (LLMs) do not update their beliefs as expected from the Bayesian framework.<n>We teach the LLMs to reason in a Bayesian manner by training them to mimic the predictions of an optimal Bayesian model.
  arXiv  Detail & Related papers  (2025-03-21T20:13:04Z)
• Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• Improve LLM-as-a-Judge Ability as a General Ability [40.2210529561692]
  Large language models (LLMs) can evaluate responses across diverse scenarios, providing accurate preference signals.<n>Recent studies have raised many methods to train LLM as generative judges, but most of them are data consuming or lack accuracy.<n>In this work, we implement a two-stage training approach, comprising supervised fine-tuning (SFT) warm-up and direct preference optimization (DPO) enhancement.
  arXiv  Detail & Related papers  (2025-02-17T11:28:43Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification [7.696798306913988]
  We introduce a framework outlining fairness regulations aligned with various fairness definitions. We explore the configuration for in-context learning and the procedure for selecting in-context demonstrations using RAG. Experiments conducted with different LLMs indicate that GPT-4 delivers superior results in terms of both accuracy and fairness compared to other models.
  arXiv  Detail & Related papers  (2024-02-28T17:29:27Z)
• Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models [54.21695754082441]
  We propose a framework to teach Large Language Models (LLMs) to generate explainable stock predictions. A reflective agent learns how to explain past stock movements through self-reasoning, while the PPO trainer trains the model to generate the most likely explanations. Our framework can outperform both traditional deep-learning and LLM methods in prediction accuracy and Matthews correlation coefficient.
  arXiv  Detail & Related papers  (2024-02-06T03:18:58Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Explanations from Large Language Models Make Small Reasoners Better [61.991772773700006]
  We show that our method can consistently and significantly outperform finetuning baselines across different settings. As a side benefit, human evaluation shows that our method can generate high-quality explanations to justify its predictions.
  arXiv  Detail & Related papers  (2022-10-13T04:50:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.