{"id":1844,"date":"2026-10-27T10:39:45","date_gmt":"2026-10-27T10:39:45","guid":{"rendered":"https:\/\/www.interviewbit.com\/varsity\/blog\/?p=1844"},"modified":"2026-10-27T10:39:49","modified_gmt":"2026-10-27T10:39:49","slug":"multimodal-llm","status":"publish","type":"post","link":"https:\/\/www.interviewbit.com\/varsity\/blog\/multimodal-llm\/","title":{"rendered":"Multimodal LLMs Explained: How Models See, Read and Reason Across Modalities"},"content":{"rendered":"\n<div class=\"wp-block-group vlf-insights is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"vlf-insights-title wp-block-paragraph\">Key Insights<\/p>\n\n\n\n<ul class=\"wp-block-list vlf-insights-list\">\n<li>Multimodal LLMs process and connect information from multiple modalities, such as text, images, audio and video, to respond to tasks that go beyond text alone.<\/li>\n\n\n\n<li>A common multimodal LLM architecture combines a vision encoder, a projector or adapter, and an LLM backbone. Implementations vary by model.<\/li>\n\n\n\n<li>Cross-attention and token fusion are different ways to integrate information across modalities; they are not necessarily mutually exclusive.<\/li>\n\n\n\n<li>Training typically involves aligning representations across modalities and instruction-tuning models to respond to user prompts about visual or other non-text inputs.<\/li>\n\n\n\n<li>Models such as GPT-4o, Gemini, Claude, LLaVA and Qwen-VL illustrate different approaches to multimodal capabilities, with varying levels of architectural transparency.<\/li>\n\n\n\n<li>Applications include visual question answering, document understanding, video analysis and AI-assisted workflows.<\/li>\n\n\n\n<li>Multimodal models can still misinterpret visual details or generate unsupported answers. Grounding and evaluation can improve reliability but do not eliminate hallucinations.<\/li>\n<\/ul>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">A multimodal large language model (multimodal LLM) is an AI system that processes information from multiple modalities, such as text, images, audio, and video, to generate responses. For example, you could upload a chart, ask the model to explain its trend, and then ask a follow-up question about a particular data point. Unlike text-only models, multimodal LLMs can incorporate visual or other non-text information into their responses, depending on their supported capabilities.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This guide explains what multimodal AI is, how multimodal LLM architecture works, how these models are trained, and where they are used. It also compares representative models and explores their limitations, including hallucinations and visual grounding.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1774\" height=\"887\" src=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/hero-illustration-showing-text-image-audio-and-video-inputs-1-1.png\" alt=\"Hero illustration showing text, image, audio and video inputs flowing into a multimodal LLM to generate an integrated response with visual analysis and key insights.\" class=\"wp-image-1846\" srcset=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/hero-illustration-showing-text-image-audio-and-video-inputs-1-1.png 1774w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/hero-illustration-showing-text-image-audio-and-video-inputs-1-1-300x150.png 300w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/hero-illustration-showing-text-image-audio-and-video-inputs-1-1-1024x512.png 1024w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/hero-illustration-showing-text-image-audio-and-video-inputs-1-1-768x384.png 768w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/hero-illustration-showing-text-image-audio-and-video-inputs-1-1-1536x768.png 1536w\" sizes=\"auto, (max-width: 1774px) 100vw, 1774px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">What is Multimodal AI, and What Is a Multimodal LLM?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal AI refers to systems that process or combine multiple types of information. A multimodal LLM uses a language-model-based system to interpret or generate responses involving different modalities. Not every multimodal AI system is an LLM.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What does Multimodal Mean in AI?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A modality is a type of information, such as text, images, audio, video, or sensor data. A unimodal system works primarily with one type, whereas multimodal AI combines information across two or more modalities.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Consider a shopping assistant that receives a product image and the question, \u201cDoes this bag have an adjustable strap?\u201d The system must interpret the visual information and connect it with the text instruction to produce a relevant answer.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How is a Multimodal LLM Different from a Text-Only LLM?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A text-only LLM primarily processes text tokens. A multimodal large language model can also incorporate representations derived from images, audio, or other supported inputs. Some models can generate outputs in additional modalities, too.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The distinction is not absolute across all systems. A vision-language model specialises in connecting visual and textual information, while multimodal LLMs can extend to audio and video. The supported inputs and outputs depend on the particular model and its architecture.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How do Multimodal LLMs Work?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Many multimodal LLMs follow a general pipeline: an encoder converts non-text input into numerical features, a projector or adapter maps those features into a compatible representation, and an LLM backbone processes the combined context to generate a response. Architectures vary, so this is a useful general model rather than a universal design.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1774\" height=\"887\" src=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/multimodal-llm-architecture-showing-input-encoders-a-project-1.png\" alt=\"Multimodal LLM architecture showing input encoders, a projector and an LLM backbone.\" class=\"wp-image-1847\" srcset=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/multimodal-llm-architecture-showing-input-encoders-a-project-1.png 1774w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/multimodal-llm-architecture-showing-input-encoders-a-project-1-300x150.png 300w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/multimodal-llm-architecture-showing-input-encoders-a-project-1-1024x512.png 1024w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/multimodal-llm-architecture-showing-input-encoders-a-project-1-768x384.png 768w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/multimodal-llm-architecture-showing-input-encoders-a-project-1-1536x768.png 1536w\" sizes=\"auto, (max-width: 1774px) 100vw, 1774px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">What does a Vision Encoder Do?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A vision encoder extracts useful features from an image, such as shapes, objects, text, and spatial relationships. Convolutional neural networks (CNNs) and Vision Transformers (ViT) are two approaches to visual representation learning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A vision transformer in multimodal AI divides an image into patches and processes them using transformer-based mechanisms. The resulting representations can be passed to later components for interpretation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/arxiv.org\/abs\/2103.00020\">CLIP research paper<\/a> introduced a method for learning aligned image and text representations using image-text pairs. This helps connect visual content with language, although alignment does not guarantee accurate interpretation of every image.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What are Projectors, Adapters and the LLM Backbone?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Visual features are not automatically compatible with a language model&#8217;s token representations. A projector or adapter can map the features into a representation the LLM can process. The LLM backbone then uses the combined context to generate a response.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">LLaVA is a research example of this approach: it connects a vision encoder with a language model to support visual instruction following. Other multimodal systems may integrate modalities differently.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Cross-Attention vs Token Fusion<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cross-attention<\/strong> allows one representation, such as text, to attend selectively to features from another modality, such as an image. This can help a model focus on visual regions relevant to a question.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Token fusion<\/strong> combines representations from different modalities into a shared sequence or token stream where the architecture supports it. The model can then process these representations together.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These mechanisms are not mutually exclusive. A multimodal model may use cross-attention at some stages and combine modality tokens at others. The implementation depends on the model&#8217;s design.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How are Multimodal LLMs Trained?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal AI training uses combinations of text and non-text data to teach models relationships across modalities. Training may involve learning visual or audio representations, aligning them with language, and adapting the model to follow user instructions. The exact pipeline differs by model family.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1774\" height=\"887\" src=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/clip-style-image-text-alignment-mapping-related-images-and-c-1.png\" alt=\"CLIP-style image-text alignment mapping related images and captions to nearby embeddings.\" class=\"wp-image-1848\" srcset=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/clip-style-image-text-alignment-mapping-related-images-and-c-1.png 1774w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/clip-style-image-text-alignment-mapping-related-images-and-c-1-300x150.png 300w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/clip-style-image-text-alignment-mapping-related-images-and-c-1-1024x512.png 1024w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/clip-style-image-text-alignment-mapping-related-images-and-c-1-768x384.png 768w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/clip-style-image-text-alignment-mapping-related-images-and-c-1-1536x768.png 1536w\" sizes=\"auto, (max-width: 1774px) 100vw, 1774px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">What is Multimodal Alignment Pre-Training?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal alignment helps a model learn relationships between different forms of information. For example, image-text pairs can teach a system to associate a photograph with a caption.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/arxiv.org\/abs\/2103.00020\">CLIP paper<\/a> demonstrates contrastive image-text learning. The <a href=\"https:\/\/proceedings.mlr.press\/v162\/li22n.html\">BLIP research paper<\/a> presents a vision-language pre-training framework designed for both understanding and generation tasks. These are examples of approaches to learning image-text relationships, not a single training pipeline used by all multimodal models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is Visual Instruction Tuning?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Visual instruction tuning trains a model on examples that combine visual inputs with instructions and responses. It helps the model learn how to answer questions about images and respond to user requests involving visual content.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/arxiv.org\/abs\/2304.08485\">LLaVA visual instruction tuning paper<\/a>, published as research in 2023, explores using generated multimodal instruction-following data to train a vision-language assistant. Broad pre-training develops representations, while instruction tuning adapts the model to follow requests more effectively.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Multimodal LLMs vs Multimodal AI: Models and Examples<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal AI is the broader category, covering systems that process several types of data. Multimodal LLMs combine multimodal processing with a large language model. The following examples illustrate different approaches and historical milestones, not a ranking or a list of the latest versions.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Model \/ family<\/th><th>Notable public milestone<\/th><th>Multimodal focus<\/th><\/tr><\/thead><tbody><tr><td>GPT-4o<\/td><td>Announced May 2024<\/td><td>Designed for real-time interaction across text, vision, and audio, with capabilities described in the <a href=\"https:\/\/openai.com\/index\/hello-gpt-4o\/\">official announcement<\/a><\/td><\/tr><tr><td>Gemini<\/td><td>Introduced December 2023<\/td><td>Built to work across text, images, audio, video, and code, as described in<a href=\"https:\/\/blog.google\/innovation-and-ai\/technology\/ai\/google-gemini-ai\/\"> <\/a><a href=\"https:\/\/blog.google\/innovation-and-ai\/technology\/ai\/google-gemini-ai\/\">Google&#8217;s announcement<\/a><\/td><\/tr><tr><td>Claude 3 family<\/td><td>Announced March 2024<\/td><td>Introduced vision capabilities for interpreting images, charts, graphs, and diagrams; see <a href=\"https:\/\/www.anthropic.com\/news\/claude-3-family\">Anthropic&#8217;s announcement<\/a><\/td><\/tr><tr><td>LLaVA<\/td><td>Research paper released April 2023<\/td><td>Connects a vision encoder and LLM for visual instruction following; see the <a href=\"https:\/\/arxiv.org\/abs\/2304.08485\">research paper<\/a><\/td><\/tr><tr><td>Qwen2.5-VL<\/td><td>Released January 2025; technical report published February 2025<\/td><td>Vision-language research covering visual recognition, document parsing, charts, and video; see the <a href=\"https:\/\/arxiv.org\/abs\/2502.13923\">Qwen technical report<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Commercial products and open research models differ in access, deployment, supported inputs, and customisation. Their capabilities also change over time, so check current official documentation before selecting a model for a project.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What are the Applications of Multimodal AI?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal LLM applications include interpreting documents, answering questions about images, analysing video, and supporting workflows that combine text with visual information. Their reliability depends on the model and the task.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Document Understanding and Visual Question Answering<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal document understanding involves extracting and interpreting information from forms, invoices, tables, charts, and scanned pages. Visual question answering (VQA) allows a user to ask questions about image content, such as identifying a trend in a chart or locating a field in a form.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These tasks may require optical character recognition (OCR), layout understanding, and visual reasoning. Performance can decline with small text, poor image quality, or complex layouts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Video Understanding and Audio-Visual Interaction<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Video understanding AI can summarise a clip, identify events, or answer questions about selected frames. Depending on the system, it may also connect video with audio or text.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Long videos often require specialised processing, such as sampling frames, preserving timestamps, or selecting relevant segments. A model should not be assumed to process every frame equally or retain every detail.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Multimodal Agents and Workflow Automation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal AI agents can combine visual inputs, text instructions, and tools to support tasks such as interpreting a software interface, assisting with quality inspection, or extracting information from documents.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Their ability to act depends on the surrounding system, tool access, permissions, and model capabilities. A model that can interpret a screenshot does not automatically have permission or reliable ability to perform every action shown in it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What are the Limitations of Multimodal LLMs?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal LLM limitations include misreading visual details, overlooking context, and generating confident answers unsupported by the input. Receiving an image or other evidence does not guarantee that a response is grounded in it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is Multimodal Hallucination?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A multimodal hallucination occurs when a model describes something that is absent from, or unsupported by, its input. For example, it might report the wrong value from a chart or describe an object that is not present in an image.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Plausible wording does not establish visual accuracy. Developers should verify outputs against the original input, especially when errors could have significant consequences.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How can Developers Improve Grounding and Reliability?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Visual grounding in AI means connecting a response to relevant evidence in an image or other visual input. Developers can improve reliability by:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Evaluating models on task-specific examples, including dense charts, small text, unusual layouts, and ambiguous images.<\/li>\n\n\n\n<li>Asking for evidence or references to relevant image regions where the system supports them.<\/li>\n\n\n\n<li>Using external validation, confidence checks, and human review for consequential decisions.<\/li>\n\n\n\n<li>Testing failure cases instead of relying on prompts alone to prevent hallucinations.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Grounding methods can help, but they do not eliminate all errors.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Multimodal LLMs extend language-model capabilities by enabling systems to work across text and other data types. Their behaviour depends on how inputs are encoded, aligned, fused, and processed, as well as how reliably responses are grounded in the evidence. Understanding the roles of vision encoders, projectors, LLM backbones, and training methods helps developers assess which model may suit a particular application.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ready to explore these concepts alongside broader generative AI techniques? Explore the<strong><a href=\"https:\/\/www.interviewbit.com\/varsity\/iit-delhi\/generative-ai\"> <\/a><\/strong><a href=\"https:\/\/www.interviewbit.com\/varsity\/iit-delhi\/generative-ai\">Generative AI programme by IIT Delhi<\/a><strong>, <\/strong>delivered through Varsity by InterviewBit<strong>,<\/strong> to build your understanding of modern AI models and their applications.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions<\/h2>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Is ChatGPT a multimodal LLM?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">ChatGPT&#8217;s multimodal capabilities depend on the model and features available. Some models can process combinations of text, images, and audio, but supported inputs and outputs vary.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">What is the difference between a multimodal LLM and a vision-language model?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Vision-language models focus on connecting visual and textual information. Multimodal LLMs can cover a broader range of modalities, including audio and video, depending on their design.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Can multimodal LLMs understand handwritten text?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Some models can interpret handwriting in images, but accuracy depends on image quality, legibility, language, and layout complexity. Important values should be checked against the source.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Do multimodal LLMs use separate models for images and text?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Some architectures use specialised encoders connected to an LLM, while others integrate modalities more directly. There is no single architecture shared by all multimodal models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">What is the difference between CLIP and LLaVA?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">CLIP learns aligned image-text representations, while LLaVA connects a vision encoder to a language model to support visual instruction following and conversational responses.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Can multimodal LLMs generate images as well as understand them?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Some systems support both image understanding and image generation. Image generation may rely on additional components or models rather than the language model alone.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">What hardware is required to run an open-source multimodal LLM?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Requirements depend on model size, quantisation, image resolution, context length, and inference setup. Smaller models may run on suitable consumer hardware, while larger models can require substantial GPU memory.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Are multimodal LLMs the same as generative AI?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. Generative AI is the broader category. Multimodal LLMs are one type of generative AI system, although not every multimodal AI system generates content.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">How are multimodal LLMs evaluated?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Evaluation can include visual question answering, document extraction, image-text alignment, instruction following, hallucination rates, and task-specific accuracy. The relevant tests depend on the intended use.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Can multimodal LLMs work with private company documents?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This depends on the model&#8217;s deployment, data-handling policies, access controls, and integrations. Before processing sensitive documents, check data retention, privacy, and security requirements.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Resources<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/openai.com\/index\/hello-gpt-4o\/\">OpenAI\u2019s GPT-4o announcement<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/blog.google\/innovation-and-ai\/technology\/ai\/google-gemini-ai\/\">Google\u2019s introduction to Gemini<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.anthropic.com\/news\/claude-3-family\">Anthropic\u2019s Claude 3 model family announcement<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2304.08485\">LLaVA: Visual Instruction Tuning research paper<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2502.13923\">Qwen2.5-VL technical report<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2103.00020\">CLIP research paper<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/proceedings.mlr.press\/v162\/li22n.html\">BLIP research paper<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Key Insights A multimodal large language model (multimodal LLM) is an AI system that processes information from multiple modalities, such as text, images, audio, and video, to generate responses. For example, you could upload a chart, ask the model to explain its trend, and then ask a follow-up question about a particular data point. Unlike [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":1884,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1844","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"acf":{"reviewed_by":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Multimodal LLMs Explained: How Models See, Read and Reason<\/title>\n<meta name=\"description\" content=\"Learn how multimodal LLMs process images, text, audio and video. 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