Awaker2.5-VL : Mise à l'échelle stable des MLLM avec un mélange d'experts efficace en paramètres
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts
Résumé
Summary
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Paper Overview
This paper introduces Awaker2.5-VL, an architecture based on a Mixture of Experts (MoE) for Multimodal Large Language Models (MLLM). Awaker2.5-VL excels in various benchmarks, showcasing superior performance in perception and overall scores compared to state-of-the-art models.
Core Contribution
Awaker2.5-VL, utilizing a MoE approach, demonstrates exceptional performance in various benchmarks, particularly in perception and overall scores, surpassing existing models. It addresses the "multi-task conflict" issue in Multimodal Large Language Models effectively.
Research Context
The research focuses on advancing Multimodal Large Language Models (MLLM) by introducing the Awaker2.5-VL architecture, emphasizing the use of a Mixture of Experts (MoE) for improved performance in visual and textual tasks simultaneously.
Keywords
- Mixture of Experts (MoE)
- Multimodal Large Language Models (MLLM)
- Benchmarking (MME-Realworld, MMBench)
- Perception and Reasoning Scores
- Expert Activation and Routing
Background
The transition from Large Language Models (LLM) to Multimodal Large Language Models (MLLM) aims to handle both textual and visual tasks concurrently. However, simple data fusion in MLLMs can lead to multi-task conflicts, necessitating innovative architectures like Awaker2.5-VL.
Research Gap
Existing MLLMs face challenges with multi-task conflicts due to straightforward data integration, highlighting the need for specialized architectures like Awaker2.5-VL with a Mixture of Experts approach.
Technical Challenges
The "multi-task conflict" issue in MLLMs poses a significant technical obstacle, requiring novel architectures like Awaker2.5-VL to efficiently handle diverse tasks without interference.
Prior Approaches
Previous approaches in MLLMs lacked the sophistication to address multi-task conflicts effectively, underscoring the necessity for advanced models like Awaker2.5-VL with its Mixture of Experts design.
Methodology
Awaker2.5-VL is built on a Mixture of Experts (MoE) framework, enhancing performance in various benchmarks through expert activation and routing strategies, such as LoRA adaptation structures.
Theoretical Foundation
Awaker2.5-VL's architecture is grounded in the Mixture of Experts (MoE) concept, where sparse expert activation for each task is crucial for efficient training and inference.
Technical Architecture
Awaker2.5-VL employs a Mixture of Experts (MoE) structure operating at the instance level, utilizing stable routing strategies to optimize task performance.
Implementation Details
The training process of Awaker2.5-VL involves three stages: Initialization, MoE Training, and Instruction Adjustment, with each expert designed as a Low-rank Adaptation structure for enhanced efficiency.
Innovation Points
Awaker2.5-VL introduces a novel Mixture of Experts (MoE) architecture with LoRA structures, showcasing superior performance in various benchmarks compared to existing models.
Experimental Validation
Awaker2.5-VL's performance is validated through experiments across multiple benchmarks, demonstrating its superiority in perception and overall scores.
Setup
Awaker2.5-VL's validation includes benchmarks like MME-RealWorld, MMBench-CN, and MME-Realworld-CN, showcasing its exceptional performance in perception and overall scores.
Metrics
Performance metrics such as perception and reasoning scores are used to evaluate Awaker2.5-VL's effectiveness in handling diverse tasks within Multimodal Large Language Models.
Results
Awaker2.5-VL outperforms competitors in various benchmarks, maintaining its lead in perception and overall scores, despite a slight decrease in reasoning compared to the state-of-the-art.
Comparative Analysis
Awaker2.5-VL's architecture, particularly its Mixture of Experts (MoE) design, surpasses competitors in benchmarks like MME-Realworld and MMBench, highlighting its technical advancements.
Impact and Implications
Awaker2.5-VL's innovative approach has significant implications for the field of Multimodal Large Language Models, paving the way for improved task handling and performance enhancements.
Key Findings
Awaker2.5-VL demonstrates exceptional performance in perception and overall scores across various benchmarks, showcasing the effectiveness of its Mixture of Experts (MoE) architecture.
Limitations
While Awaker2.5-VL excels in perception and overall scores, there is a slight decrease in reasoning compared to the state-of-the-art, indicating areas for further improvement.
Future Directions
Future research directions include enhancing query representations for improved routing performance and extending the MoE model to the ViT part of the multimodal model, presenting concrete opportunities for advancement.
Practical Significance
Awaker2.5-VL's advancements have practical implications for real-world applications, offering improved performance in handling diverse textual and visual tasks simultaneously.