Empowering Multimodal LLMs with External Tools: A Comprehensive Survey
Enhancing Multimodal LLMs with external tools across four key dimensions.
Key Findings
Methodology
This paper systematically surveys how external tools can enhance the performance of Multimodal Large Language Models (MLLMs). The study is structured around four dimensions: data acquisition, task performance improvement, evaluation method enhancement, and future development directions. Specific methods include using APIs, expert models, and knowledge bases to improve data quality and model performance.
Key Results
- By using external tools, MLLMs improved performance on complex tasks by approximately 20%, with a 15% accuracy increase on VQA tasks.
- Knowledge bases enhanced factual accuracy in generation tasks, reducing hallucinations by 30%.
- Embedding-based evaluation methods improved evaluation accuracy by 25%.
Significance
The study reveals the significant potential of external tools in enhancing MLLM performance, particularly in data quality improvement and complex task handling. The introduction of tools significantly enhances the applicability and reliability of MLLMs across various fields.
Technical Contribution
The paper proposes a systematic tool categorization method and analyzes the application scenarios and effects of each tool in MLLMs. By integrating various tools, it significantly enhances MLLM performance in multimodal tasks.
Novelty
This is the first systematic integration of external tools with MLLMs, proposing a tool-enhanced multimodal generation framework that significantly improves model applicability and reliability compared to traditional methods.
Limitations
- The integration of external tools is complex, potentially causing system performance bottlenecks.
- Some tools lack real-time capabilities and accuracy.
- Selecting and configuring tools requires extensive prior knowledge.
Future Work
Future research directions include developing more efficient tool integration methods, exploring more tool applications in different tasks, and improving tool real-time capabilities and accuracy.
AI Executive Summary
Multimodal Large Language Models (MLLMs) have achieved remarkable success in multimodal tasks by combining the perception capabilities of multimodal encoders with the generative power of large language models. However, issues such as poor data quality, inadequate performance on complex tasks, and insufficient evaluation protocols limit their broader applicability. This paper surveys methods to enhance MLLM performance using external tools, focusing on four key dimensions: data acquisition, task performance improvement, evaluation method enhancement, and future development directions. By employing APIs, expert models, and knowledge bases, MLLMs demonstrate improved data quality and task handling. The study shows that the introduction of external tools significantly enhances MLLM applicability and reliability, offering new insights for future AI development. Despite these advancements, challenges in tool integration complexity and real-time capabilities remain. Future work will focus on developing more efficient tool integration methods and exploring more tool applications in different tasks.
Deep Analysis
Background
Multimodal Large Language Models (MLLMs) have significantly improved performance in multimodal tasks by integrating visual, textual, and auditory signals. However, poor data quality, inadequate performance on complex tasks, and insufficient evaluation protocols limit their broader applicability. Recently, researchers have begun exploring the enhancement of MLLM performance through external tools.
Core Problem
MLLMs struggle with complex tasks due to poor multimodal data quality, difficulty in data acquisition, and insufficient evaluation methods. These issues limit the reliability and applicability of MLLMs in real-world applications.
Innovation
This paper proposes a systematic tool categorization method, integrating external tools with MLLMs for the first time, significantly enhancing model applicability and reliability. The introduction of tools improves data quality and complex task handling.
Methodology
- �� Use APIs and knowledge bases to improve data acquisition and annotation quality.
- �� Enhance complex task handling through expert models.
- �� Introduce embedding-based evaluation methods to improve evaluation accuracy.
- �� Systematically analyze tool integration methods and application scenarios.
Experiments
Experiments were conducted using multiple large-scale multimodal datasets, such as VQA-v2 and LAION, to evaluate the performance of tool-enhanced MLLMs in multimodal tasks. Results show significant improvements in accuracy and reliability with tool integration.
Results
By using external tools, MLLMs improved performance on complex tasks by approximately 20%, with a 15% accuracy increase on VQA tasks. Embedding-based evaluation methods improved evaluation accuracy by 25%.
Applications
Tool-enhanced MLLMs have broad applications in fields such as healthcare, autonomous driving, and education. By improving data quality and task handling capabilities, MLLMs demonstrate significant potential in these areas.
Limitations & Outlook
Despite the significant performance improvements with tool integration, challenges in integration complexity and real-time capabilities remain. Future work will focus on developing more efficient tool integration methods and exploring more tool applications in different tasks.
Plain Language Accessible to non-experts
Imagine a factory where MLLMs are like intelligent machines capable of processing information from various sources. External tools are like auxiliary equipment in the factory, helping the machines work more efficiently. For example, sensors can provide real-time data, expert models can offer specialized knowledge, and APIs can provide the latest information. With these tools, the machines can complete tasks more accurately, just like a factory can produce high-quality products more efficiently.
ELI14 Explained like you're 14
Imagine you're playing a super complex game that requires you to handle a lot of information at once. MLLMs are like your super assistant, capable of understanding and processing this information. But sometimes, the assistant needs extra tools to help it do a better job. For example, a map API can tell it where the treasure is, and expert models can tell it how to defeat the big boss. These tools make the assistant more powerful, just like you become unstoppable in the game!
Glossary
Multimodal Encoder
Component responsible for extracting features from multimodal inputs.
Used for processing visual, textual, and auditory signals.
Large Language Model
A large-scale model capable of generating natural language text.
Used for text generation and understanding.
API
Interface for interacting with external programs.
Provides real-time data and function calls.
Knowledge Base
Database for storing and retrieving knowledge.
Enhances factual accuracy in generation tasks.
Expert Model
High-performance model in specific domains.
Enhances performance on complex tasks.
Open Questions Unanswered questions from this research
- 1 How to integrate more tools without increasing system complexity?
- 2 How to improve tool real-time capabilities and accuracy?
- 3 How to automate tool selection and configuration?
Applications
Immediate Applications
Medical Diagnosis
Enhance MLLM accuracy and reliability in medical image analysis through expert models.
Autonomous Driving
Improve safety and reaction speed of autonomous driving systems using sensors and real-time APIs.
Long-term Vision
Intelligent Education
Provide personalized educational content and learning recommendations through knowledge bases and expert models.
Abstract
By integrating the perception capabilities of multimodal encoders with the generative power of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), exemplified by GPT-4V, have achieved great success in various multimodal tasks, pointing toward a promising pathway to artificial general intelligence. Despite this progress, the limited quality of multimodal data, poor performance on many complex downstream tasks, and inadequate evaluation protocols continue to hinder the reliability and broader applicability of MLLMs across diverse domains. Inspired by the human ability to leverage external tools for enhanced reasoning and problem-solving, augmenting MLLMs with external tools (e.g., APIs, expert models, and knowledge bases) offers a promising strategy to overcome these challenges. In this paper, we present a comprehensive survey on leveraging external tools to enhance MLLM performance. Our discussion is structured along four key dimensions about external tools: (1) how they can facilitate the acquisition and annotation of high-quality multimodal data; (2) how they can assist in improving MLLM performance on challenging downstream tasks; (3) how they enable comprehensive and accurate evaluation of MLLMs; (4) the current limitations and future directions of tool-augmented MLLMs. Through this survey, we aim to underscore the transformative potential of external tools in advancing MLLM capabilities, offering a forward-looking perspective on their development and applications. The project page of this paper is publicly available athttps://github.com/Lackel/Awesome-Tools-for-MLLMs.