Grandes Modelos de Linguagem Multimodais (MLLMs): Da Teoria \`a Pr\'atica

2026-02-16 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Computer Vision · Depth: Intermediate, quick

Summary

Multimodal Large Language Models (MLLMs) integrate the natural language processing strengths of Large Language Models with perception capabilities across modalities like image and audio, marking a significant evolution in artificial intelligence. This chapter outlines the core principles of MLLMs and showcases prominent models in the field. It also delves into practical methodologies for data preprocessing, effective prompt engineering, and constructing multimodal AI pipelines using frameworks such as LangChain and LangGraph. Supplementary materials are accessible online for hands-on learning. The discussion concludes by addressing current challenges and identifying future trends within MLLM development.

Key takeaway

For AI engineers and machine learning practitioners building advanced AI systems, understanding MLLM fundamentals and practical implementation techniques is essential. You should explore frameworks like LangChain and LangGraph to develop robust multimodal pipelines, leveraging the provided supplementary materials for hands-on experience. This approach will enable you to integrate diverse data types and enhance AI system capabilities beyond text-only interactions.

Key insights

MLLMs merge LLM language capabilities with multimodal perception, advancing AI's understanding of diverse data.

Principles

• MLLMs combine language understanding with image/audio perception.
• Effective prompt engineering is crucial for MLLM performance.

Method

The chapter explores practical techniques for preprocessing multimodal data, engineering prompts, and building AI pipelines using LangChain and LangGraph frameworks.

In practice

• Utilize LangChain for multimodal pipeline construction.
• Implement LangGraph for complex MLLM workflows.

Topics

• Multimodal Large Language Models
• Prompt Engineering
• LangChain
• Multimodal Pipelines
• Natural Language Processing

Code references

• neemiasbsilva/MLLMs-Teoria-e-Pratica

Best for: AI Engineer, Machine Learning Engineer, AI Student

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• Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages — Multimodal LLMs must integrate text, speech, and vision for low-resource languages, moving beyond English-centric benchmarks.
• From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models — The survey unifies vision-language perception in MLLMs, proposing a five-stage evolution taxonomy and future directions for AGI.
• Comparative Evaluation of Machine Translation Systems on Images with Text — MLLMs excel in image-to-text translation, outperforming other paradigms through enhanced contextual understanding.
• MLLM-Microscope: Unlocking Hidden Structure Within Multimodal Large Language Models — MLLM-Microscope reveals MLLM internal representation dynamics, showing modality fusion impacts linearity, dimension, and anisotropy across transformer layers.
• Social Norm Reasoning in Multimodal Language Models: An Evaluation — MLLMs demonstrate stronger social norm reasoning in text than images, with GPT-4o leading performance.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.