A Comprehensive Survey on Multimodal Large Language Models: Techniques, Applications, and Future Directions

Conceptos Básicos

Multimodal Large Language Models (MLLMs) have emerged as a promising approach to achieving artificial general intelligence by leveraging the power of large language models and multimodal reasoning. This survey provides a comprehensive overview of the recent progress in MLLMs, including key techniques such as Multimodal Instruction Tuning, Multimodal In-Context Learning, Multimodal Chain of Thought, and LLM-Aided Visual Reasoning.

Resumen

This survey provides a comprehensive overview of the recent progress in Multimodal Large Language Models (MLLMs). It starts by introducing the formulation of MLLMs and delineating their related concepts.

The key techniques and applications of MLLMs are then discussed in detail:

1. Multimodal Instruction Tuning (M-IT):

   • Focuses on adapting pre-trained language models for multimodality through architectural and data-driven approaches.
   • Covers methods for data collection, including benchmark adaptation, self-instruction, and hybrid composition.
   • Discusses various modality bridging techniques, such as learnable interfaces and expert models.
   • Introduces evaluation methods for instruction-tuned MLLMs, including closed-set and open-set assessments.

2. Multimodal In-Context Learning (M-ICL):

   • Extends the successful in-context learning paradigm from unimodal language models to the multimodal domain.
   • Leverages a few demonstration examples to enable few-shot learning on various visual reasoning tasks.
   • Explores the use of M-ICL in solving complex tasks and teaching LLMs to use external tools.

3. Multimodal Chain of Thought (M-CoT):

   • Builds upon the success of chain of thought reasoning in language models and extends it to the multimodal setting.
   • Discusses modality bridging approaches, including learnable interfaces and expert models.
   • Covers different learning paradigms, such as finetuning, few-shot, and zero-shot, for acquiring M-CoT abilities.
   • Examines the configuration of reasoning chains and generation patterns.

4. LLM-Aided Visual Reasoning (LAVR):

   • Explores the use of large language models as helpers in visual reasoning tasks.
   • Categorizes LAVR systems based on their training paradigms, including training-free and finetuning approaches.
   • Identifies the main roles that LLMs play in these systems, such as controllers, decision makers, and semantics refiners.
   • Discusses evaluation methods, including benchmark metrics and manual assessments.

Finally, the survey concludes by highlighting the current challenges and pointing out promising research directions for the future development of MLLMs.

Estadísticas

Recent years have seen the remarkable progress of large language models, which raise amazing emergent abilities, typically including In-Context Learning, instruction following, and Chain of Thought.
Multimodal Large Language Models (MLLMs) have emerged as a promising approach to achieving artificial general intelligence by leveraging the power of large language models and multimodal reasoning.
MLLMs can generally support a larger spectrum of tasks compared to unimodal language models, as they can receive and reason with multimodal information.
GPT-4 has ignited a research frenzy over MLLMs due to the amazing examples it has shown, though its multimodal interface is not publicly available.

Citas

"MLLM is more in line with the way humans perceive the world. Our humans naturally receive multisensory inputs that are often complementary and cooperative. Therefore, multimodal information is expected to make MLLM more intelligent."
"MLLM offers a more user-friendly interface. Thanks to the support of multimodal input, users can interact and communicate with the intelligent assistant in a more flexible way."
"MLLM is a more well-rounded task-solvers. While LLMs can typically perform NLP tasks, MLLMs can generally support a larger spectrum of tasks."

Ideas clave extraídas de

A Survey on Multimodal Large Language Models

by Shukang Yin,... a las arxiv.org 04-02-2024

https://arxiv.org/pdf/2306.13549.pdf

Consultas más profundas

How can MLLMs be further improved to overcome the current limitations in perception capabilities, reasoning chain robustness, and instruction-following abilities?

Perception Capabilities:

Enhanced Alignment Pre-training: Improving the alignment between visual and textual modalities through more fine-grained alignment pre-training methods, such as utilizing large vision foundation models like SAM to compress visual information efficiently.
Local Feature Extraction: Incorporating methods to extract local features from images to provide more detailed information to MLLMs, potentially reducing information loss and enhancing perception capabilities.

Reasoning Chain Robustness:

Multimodal Reasoning Enhancement: Developing methods to improve multimodal reasoning by ensuring that the reasoning ability of MLLMs after receiving visual information is on par with their unimodal reasoning capabilities.
Robust Reasoning Chains: Implementing strategies to ensure the robustness of reasoning chains, potentially by introducing more sophisticated reasoning mechanisms or training methods.

Instruction-Following Abilities:

Generalization Improvement: Broadening the scope of tasks covered during instruction tuning to enhance the generalization capabilities of MLLMs.
Task-Specific Training: Curating specific datasets for instruction-following tasks to provide targeted training and improve the model's ability to generate expected answers based on explicit instructions.

What are the potential ethical and safety concerns associated with the rapid development of MLLMs, and how can they be addressed?

Ethical Concerns:

Bias and Fairness: MLLMs may perpetuate biases present in training data, leading to biased outputs. Addressing this requires diverse and representative training data and ongoing bias detection and mitigation strategies.
Privacy: MLLMs may inadvertently expose sensitive information shared in interactions. Implementing robust data privacy measures and transparency in data handling can mitigate privacy risks.

Safety Concerns:

Misinformation: MLLMs can propagate misinformation if not monitored carefully. Implementing fact-checking mechanisms and verification processes can help combat this issue.
Malicious Use: MLLMs can be exploited for malicious purposes like generating harmful content. Implementing strict usage policies, ethical guidelines, and monitoring systems can help prevent misuse.

Given the complementary strengths of language models and vision models, how can the integration of MLLMs and other modalities, such as audio and tactile, lead to more comprehensive and intelligent multimodal systems?

Audio Integration:

Speech Recognition: Integrating audio input for tasks like speech recognition can enhance user interaction and accessibility.
Sound Understanding: Incorporating audio cues can improve context understanding in tasks like video analysis or environmental monitoring.

Tactile Integration:

Haptic Feedback: Integrating tactile feedback can enhance user experience in virtual environments or assistive technologies.
Tactile Object Recognition: Combining tactile input with visual and textual information can improve object recognition tasks, especially in robotics and healthcare applications.

Comprehensive Multimodal Systems:

Fusion of Modalities: Integrating audio, visual, textual, and tactile modalities can provide a holistic understanding of the environment, leading to more robust and context-aware systems.
Cross-Modal Learning: Leveraging the strengths of each modality through cross-modal learning can enhance the overall intelligence and adaptability of multimodal systems.