Awesome-Personalized-Alignment

1 Papers

1.1 Position and Survey Paper

• [2024/12] Personalized Multimodal Large Language Models: A Survey [arXiv]
• [2024/11] Personalization of Large Language Models: A Survey [arXiv]
• [2024/10] When large language models meet personalization: perspectives of challenges and opportunities [World Wide Web Journal]
• [2024/07] The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm [arXiv]
• [2024/04] The benefits, risks and bounds of personalizing the alignment of large language models to individuals [Nature Machine Intelligence]
• [2024/02] Position: A Roadmap to Pluralistic Alignment [ICML 2024]

1.2 Personalized Preference

• [2024/12] AI PERSONA: Towards Life-long Personalization of LLMs [arXiv]
• [2024/11] BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization [EMNLP 2024]
• [2024/10] Large Language Models Empowered Personalized Web Agents [arXiv]
• [2024/10] ComPO: Community Preferences for Language Model Personalization [arXiv]
• [2024/10] MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time [arXiv]
• [2024/10] LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education [arXiv]
• [2024/10] Personalized Adaptation via In-Context Preference Learning [arXiv]
• [2024/10] Aligning LLMs with Individual Preferences via Interaction [arXiv]
• [2024/10] Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements [arXiv]
• [2024/10] PAD: Personalized Alignment at Decoding-Time [arXiv]
• [2024/10] MAP: Multi-Human-Value Alignment Palette [OpenReview]
• [2024/10] PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment [OpenReview]
• [2024/09] PersonalLLM: Tailoring LLMs to Individual Preferences [arXiv]
• [2024/08] Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement [arXiv]
• [2024/08] Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning [arXiv]
• [2024/06] Show, Don't Tell: Aligning Language Models with Demonstrated Feedback [arXiv]
• [2024/06] Few-shot Personalization of LLMs with Mis-aligned Responses [arXiv]
• [2024/06] Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration [EMNLP 2024]
• [2024/05] Aligning to Thousands of Preferences via System Message Generalization [arXiv]
• [2024/05] RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation [arXiv]
• [2024/04] The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models [arXiv]
• [2024/02] Personalized Language Modeling from Personalized Human Feedback [arXiv]
• [2023/10] Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging [arXiv]
• [2024/09] Everyone Deserves A Reward: Learning Customized Human Preferences [arXiv]

1.2 Personalized Generation

• [2024/10] Personalized Visual Instruction Tuning [arXiv]
• [2024/06] Yo'LLaVA: Your Personalized Language and Vision Assistant [arXiv]
• [2024/05] PMG: Personalized Multimodal Generation with Large Language [WWW 2024]

2 Workshops, Tutorials

• [2024/12] Pluralistic Alignment [Pluralistic Alignment @ NeurIPS 2024]