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Also known as: VLM
A large language model extended to process or generate multiple modalities such as text, images, audio, or video.
DESCRIPTION
Multimodal LLMs augment text LLMs with encoders and decoders for other modalities, enabling perception and generation across inputs. They can accept images for grounded dialogue, listen to audio, or generate images and videos from text. Training often combines modality-specific pretraining with joint instruction tuning. Applications span assistants, agents, creative tools, accessibility, and scientific analysis. Safety and evaluation remain active research areas, particularly for hallucinations and harmful content across modalities.
ALTERNATIVE DEFINITIONS
OpenAI
GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.
Defines multimodality via image and text inputs within an LLM framework.
Gemini is Google’s most capable and general model yet, built from the ground up to be multimodal, capable of reasoning across text, images, audio, video, and code.
Highlights native multimodality and broad modality coverage.
RELATED TERMS
GPT-4V Gemini Flamingo Perceiver Audio-Text Model Text-to-Image Instruction Tuning Function Calling Retrieval-Augmented Generation
COMPETING TERMS
Text-only LLM
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager Chief Technology Officer
EXTERNAL SOURCES
- Wikipedia - Large language model
- OpenAI - GPT-4 overview with multimodal info
- Google - Gemini multimodal model overview
- arXiv - Flamingo multimodal few-shot learning
TOP QUESTIONS
What makes a Multimodal LLM different from a Vision Language Model?
A Multimodal LLM aims to handle multiple modalities beyond images and text, such as audio and video. A VLM typically focuses on vision-plus-text tasks.
Category: Comparison
What are common applications of Multimodal LLMs?
Applications include visual assistants, audio transcription with reasoning, image generation, code assistants with screenshots, and video understanding for analytics or creative tools.
Category: Examples
How are Multimodal LLMs trained?
They use modality encoders and joint training objectives, followed by instruction tuning to align behaviors. Large-scale curated datasets and synthetic prompts are common.
Category: HowTo
Why are Multimodal LLMs important now?
They enable AI to interact with the world in richer ways, improving grounding, accessibility, and utility. Rapid model and data scaling have made them practical.
Category: Relevancy
What risks are unique to Multimodal LLMs?
They may misinterpret images or audio, propagate biases from web data, and leak sensitive information in multimedia inputs. Governance and red-teaming are essential.
Category: Risks
Which models are flagship Multimodal LLMs?
GPT-4V and Gemini are prominent examples, alongside research models like Flamingo, LLaVA, and Kosmos. Their capabilities vary by modality coverage.
Category: Examples
How do Multimodal LLMs perform zero-shot tasks?
Contrastive or unified pretraining aligns modalities, letting models generalize to novel instructions without task-specific fine-tuning.
Category: Concepts
What is the biggest misconception about Multimodal LLMs?
Many assume they fully understand images like humans; in practice, they can hallucinate or miss fine-grained details without targeted training or tools.
Category: Misunderstanding
How can teams start with a Multimodal LLM?
Begin with hosted APIs or open-source VLMs, curate datasets for your domain, and implement guardrails for safety and privacy.
Category: Learning