# Yo'LLaVA: Your Personalized Language and Vision Assistant

arXiv: https://arxiv.org/abs/2406.09400 · HTML: https://mlsanity.com/paper/2406.09400/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 31 of its 43 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 31)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://mlsanity.com/paper/2102.08981/) (2021)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://mlsanity.com/paper/2302.03668/) (2023)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://mlsanity.com/paper/2305.11554/) (2023)
- [Foundational Challenges in Assuring Alignment and Safety of Large Language Models](https://mlsanity.com/paper/2404.09932/) (2024)
- [Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models](https://mlsanity.com/paper/1909.11299/) (2019)
- [Investigating the Catastrophic Forgetting in Multimodal Large Language Models](https://mlsanity.com/paper/2309.10313/) (2023)
- [Smart Mining for Deep Metric Learning](https://mlsanity.com/paper/1704.01285/) (2017)
- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://mlsanity.com/paper/2004.05388/) (2020)
- [Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts](https://mlsanity.com/paper/2112.08348/) (2021)
- [MyVLM: Personalizing VLMs for User-Specific Queries](https://mlsanity.com/paper/2403.14599/) (2024)

## Built on by (most cited first, 30 of 62)

- [Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models](https://mlsanity.com/paper/2410.03290/) (2024)
- [UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens](https://mlsanity.com/paper/2505.14671/) (2025)
- [MC-LLaVA: Multi-Concept Personalized Vision-Language Model](https://mlsanity.com/paper/2411.11706/) (2024)
- [M2A: Multimodal Memory Agent with Dual-Layer Hybrid Memory for Long-Term Personalized Interactions](https://mlsanity.com/paper/2602.07624/) (2026)
- [RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models](https://mlsanity.com/paper/2410.13360/) (2024)
- [MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge](https://mlsanity.com/paper/2502.19870/) (2025)
- [YoChameleon: Personalized Vision and Language Generation](https://mlsanity.com/paper/2504.20998/) (2025)
- [Personalized Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2412.02142/) (2024)
- [Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink](https://mlsanity.com/paper/2501.15269/) (2025)
- [MMPB: It's Time for Multi-Modal Personalization](https://mlsanity.com/paper/2509.22820/) (2025)
- [Personalized Vision via Visual In-Context Learning](https://mlsanity.com/paper/2509.25172/) (2025)
- [PVChat: Personalized Video Chat with One-Shot Learning](https://mlsanity.com/paper/2503.17069/) (2025)
- [RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models](https://mlsanity.com/paper/2506.18369/) (2025)
- [Personalized Large Vision-Language Models](https://mlsanity.com/paper/2412.17610/) (2024)
- [PersonaVLM: Long-Term Personalized Multimodal LLMs](https://mlsanity.com/paper/2604.13074/) (2026)
- [Training-Free Personalization via Retrieval and Reasoning on Fingerprints](https://mlsanity.com/paper/2503.18623/) (2025)
- [Personalized Representation from Personalized Generation](https://mlsanity.com/paper/2412.16156/) (2024)
- [Personalization Toolkit: Training Free Personalization of Large Vision Language Models](https://mlsanity.com/paper/2502.02452/) (2025)
- [Unified Personalized Understanding, Generating and Editing](https://mlsanity.com/paper/2601.06965/) (2026)
- [Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes](https://mlsanity.com/paper/2505.19582/) (2025)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)
- [Online-PVLM: Advancing Personalized VLMs with Online Concept Learning](https://mlsanity.com/paper/2511.20056/) (2025)
- [Concept-as-Tree: A Controllable Synthetic Data Framework Makes Stronger Personalized VLMs](https://mlsanity.com/paper/2503.12999/) (2025)
- [Small-Large Collaboration: Training-efficient Concept Personalization for Large VLM using a Meta Personalized Small VLM](https://mlsanity.com/paper/2508.07260/) (2025)
- [Jarvis: Towards Personalized AI Assistant via Personal KV-Cache Retrieval](https://mlsanity.com/paper/2510.22765/) (2025)
- [Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning](https://mlsanity.com/paper/2502.20587/) (2025)
- [Personal Visual Context Learning in Large Multimodal Models](https://mlsanity.com/paper/2605.10936/) (2026)
- [Personalize Your Large Vision-language Models With In-context Prompt Tuning](https://mlsanity.com/paper/2605.31513/) (2026)
- [Aligning VLM Assistants with Personalized Situated Cognition](https://mlsanity.com/paper/2506.00930/) (2025)
- [Improving Personalized Search with Regularized Low-Rank Parameter Updates](https://mlsanity.com/paper/2506.10182/) (2025)

## Abstract (arXiv)

Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering). While broad, their knowledge remains generic (e.g., recognizing a dog), and they are unable to handle personalized subjects (e.g., recognizing a user's pet dog). Human reasoning, in contrast, typically operates within the context of specific subjects in our surroundings. For example, one might ask, "What should I buy for my dog's birthday?"; as opposed to a generic inquiry about "What should I buy for a dog's birthday?". Similarly, when looking at a friend's image, the interest lies in seeing their activities (e.g., "my friend is holding a cat"), rather than merely observing generic human actions (e.g., "a man is holding a cat"). In this paper, we introduce the novel task of personalizing LMMs, so that they can have conversations about a specific subject. We propose Yo'LLaVA, which learns to embed a personalized subject into a set of latent tokens given a handful of example images of the subject. Our qualitative and quantitative analyses reveal that Yo'LLaVA can learn the concept more efficiently using fewer tokens and more effectively encode the visual attributes compared to strong prompting baselines (e.g., LLaVA).