ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant

arXiv:2505.03654 · cs.CV, cs.AI · Submitted 2025-05-06 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-05-06

Updated: 2026-08-29

Comments: accepted in EMNLP 2026

Code: https://github.com/xyfyyds/ReGraP

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

Terminology

• Knowledge Graph — 32× in this paper · explained in 7 episodes
• Benchmark — 30× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Personalization — 12× in this paper · explained in 2 episodes
• Knowledge graphs — 9× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Graph Construction — 6× in this paper · explained in 3 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Embeddings — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Republic of Gamers — 3× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Contextual Understanding — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Qwen Technical Report
• Gemini: A Family of Highly Capable Multimodal Models
• Personalized Multimodal Large Language Models: A Survey
• Personalized Large Vision-Language Models
• RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models
• LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
• GPT-4 Technical Report
• DeepSeek-V3 Technical Report
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Training-Free Personalization via Retrieval and Reasoning on Fingerprints
• Personalization Toolkit: Training Free Personalization of Large Vision Language Models
• Concept-as-Tree: A Controllable Synthetic Data Framework Makes Stronger Personalized VLMs
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
• GPT-4o System Card
• Adam: A Method for Stochastic Optimization
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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