# LLMBind: A Unified Modality-Task Integration Framework arXiv: https://arxiv.org/abs/2402.14891 ยท HTML: https://mlsanity.com/paper/2402.14891/ ## Key facts - Cited by 7 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 65 of its 91 references that are in the map. - First posted on arXiv 22 Feb 2024. ## Placed in - Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/) - Mixture-of-experts / [Multimodal MoE models](https://mlsanity.com/moe/multimodal-moe-models/) ## Builds on (most cited first, 30 of 65) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021) - [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017) - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021) - [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023) - [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022) - [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021) - [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022) - [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020) - [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022) - [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023) - [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023) - [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022) - [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023) - [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015) - [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016) - [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021) - [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021) - [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023) - [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023) - [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022) - [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023) ## Built on by (most cited first, 7 of 7) - [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024) - [Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine](https://mlsanity.com/paper/2412.09278/) (2024) - [Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks](https://mlsanity.com/paper/2409.08087/) (2024) - [Life-Cycle Routing Vulnerabilities of LLM Router](https://mlsanity.com/paper/2503.08704/) (2025) - [LangBridge: Interpreting Image as a Combination of Language Embeddings](https://mlsanity.com/paper/2503.19404/) (2025) - [Align 3D Representation and Text Embedding for 3D Content Personalization](https://mlsanity.com/paper/2508.16932/) (2025) - [UniAfford: Token-Routed Multitask Learning for Generalizable 2D-3D Affordance Perception](https://mlsanity.com/paper/2609.37264/) (2026) ## Abstract (arXiv) Despite recent progress in Multi-Modal Large Language Models (MLLMs), it remains challenging to integrate diverse tasks ranging from pixel-level perception to high-fidelity generation. Existing approaches often suffer from either restricted task extensibility or severe performance degradation due to modality interference. n this paper, we present LLMBind, an extensible framework that unifies multimodal tasks through a dual-pathway mechanism: In-Situ semantic embeddings for localization-sensitive tasks like semantic segmentation and Ex-Situ task-prompts for generation across image, video, and audio modalities. Additionally, we employ a Mixture-of-Experts (MoE) architecture to route task-specific tokens, thereby achieving modality disentanglement and mitigating negative transfer. We also curate a 400k multi-turn interactive dataset focused on iterative visual refinement to enable human-like interaction. Extensive experiments demonstrate that LLMBind achieves excellent performance across multiple perception and generation benchmarks while maintaining superior expandability.