Learning Personalized Prompts for Healthcare Guidance

arXiv:2412.15957 · cs.CL, cs.AI, cs.IR · Submitted 2024-12-20 · Read on arXiv

cs.CL, cs.AI, cs.IR

Submitted: 2024-12-20

Updated: 2026-08-31

Comments: Accepted at the EMNLP 2026 Main Conference

Code: https://github.com/CGCL-codes/PPL

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Personalization — 36× in this paper · explained in 2 episodes
• Red Blood Cell — 7× in this paper
• White Blood Cell — 7× in this paper
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Gestational Diabetes Mellitus — 1× in this paper
• Langevin Dynamics — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Qwen Technical Report
• GPT-4 Technical Report
• Language Models are Few-Shot Learners
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• The Llama 3 Herd of Models
• ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
• A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
• Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging
• Adam: A Method for Stochastic Optimization
• PRewrite: Prompt Rewriting with Reinforcement Learning
• Personalized Language Modeling from Personalized Human Feedback
• The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
• LLaMA: Open and Efficient Foundation Language Models
• A Survey for Large Language Models in Biomedicine
• Personalized Large Language Models
• Personalization of Large Language Models: A Survey
• LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

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