# Empathy by Design: Aligning Large Language Models for Healthcare Dialogue

arXiv: https://arxiv.org/abs/2512.06097 · HTML: https://mlsanity.com/paper/2512.06097/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- LLM alignment / RLHF / preference optimisation / [Medical LLM alignment](https://mlsanity.com/alignment/medical-llm-alignment/)

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review](https://mlsanity.com/paper/2405.02559/) (2024)
- [Don't lose the message while paraphrasing: A study on content preserving style transfer](https://mlsanity.com/paper/2308.09055/) (2023)
- [Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions](https://mlsanity.com/paper/2405.16402/) (2024)
- [Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models](https://mlsanity.com/paper/2307.04910/) (2023)
- [Development of a Reliable and Accessible Caregiving Language Model (CaLM)](https://mlsanity.com/paper/2403.06857/) (2024)
- [When Patients Go to "Dr. Google" Before They Go to the Emergency Department](https://mlsanity.com/paper/2510.03329/) (2025)

## Built on by (most cited first, 1 of 1)

- [ADRD-Bench: A Preliminary LLM Benchmark for Alzheimer's Disease and Related Dementias](https://mlsanity.com/paper/2602.11460/) (2026)

## Abstract (arXiv)

General-purpose large language models (LLMs) have demonstrated remarkable generative and reasoning capabilities but remain limited in healthcare and caregiving applications due to two key deficiencies: factual unreliability and a lack of empathetic communication. These shortcomings pose significant risks in sensitive contexts where users, particularly non-professionals and caregivers, seek medically relevant guidance or emotional reassurance. To address these challenges, we introduce a Direct Preference Optimization (DPO)-based alignment framework designed to improve factual correctness, semantic coherence, and human-centric qualities such as empathy, politeness, and simplicity in caregiver-patient dialogues. Our approach fine-tunes domain-adapted LLMs using pairwise preference data, where preferred responses reflect supportive and accessible communication styles while rejected ones represent prescriptive or overly technical tones. This direct optimization method aligns model outputs with human preferences more efficiently than traditional reinforcement-learning-based alignment. Empirical evaluations across multiple open and proprietary LLMs show that our DPO-tuned models achieve higher semantic alignment, improved factual accuracy, and stronger human-centric evaluation scores compared to baseline and commercial alternatives such as Google medical dialogue systems. These improvements demonstrate that preference-based alignment offers a scalable and transparent pathway toward developing trustworthy, empathetic, and clinically informed AI assistants for caregiver and healthcare communication. Our open-source code is available at: https://github.com/LeonG19/Empathy-by-Design