LLMBind: A Unified Modality-Task Integration Framework

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.

Bin Zhu, Munan Ning, Peng Jin, Bin Lin, Jinfa Huang, Qi Song, Junwu Zhang, Zhenyu Tang, Mingjun Pan, Li Yuan · 2024