The Next Chapter in LLM: Large Multimodal Models Leading the Way (04/12)

A new era is unfolding in the field of artificial intelligence. ChatGPT has now become an integral part of our daily lives, and many companies are diving into technology development with a keen interest in Large Language Models (LLMs). While language models focusing on text have been actively researched until now, recent advancements are expanding into unimaginable dimensions.

One notable development is the emergence of Large Multimodal Models (LMMs), which introduce a new paradigm by considering various types of data such as text, images, audio, and more. This shift signifies a significant leap forward in how AI systems comprehend and interact with diverse forms of information, paving the way for exciting possibilities yet to be fully explored.

What is LLM and Limitations Does It Have?

LLM” stands for “Large Language Models,” referring to models designed for large-scale natural language processing and understanding. Particularly, LLMs focus on understanding and generating text-based information, with OpenAI’s GPT series being a prominent example. These models are primarily trained on vast text corpora and are proficient in understanding and generating human language in various contexts. However, inherently, LLMs do not process non-textual data such as images or audio. Therefore, tasks that can be performed using LLM applications are predominantly centered around text, including article writing, language translation, question answering, document summarization, and text-based content generation. In addition, because LLM training data may include biased or incorrect information alongside factual data, bias or hallucination phenomena can occur. 

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What can Large Multimodal Models (LMM) do?

Large Multimodal Models (LMM) represent a groundbreaking advancement in artificial intelligence, capable of processing and understanding various types of data simultaneously. Unlike traditional models that focus solely on text, LMMs integrate information from multiple modalities such as images, text, audio, and video. This enables them to comprehend and generate responses based on a richer context, leading to more nuanced and accurate results. LMM is a model capable of processing and understanding various types of data formats, with OpenAI’s recent release of ‘GPT-4V’ being a prominent example. open-source model called LLAVA 1.5 is also available.

LMM excels in tasks requiring multimodal inputs. By analyzing both the visual and auditory components of videos, LMMs can generate concise summaries or extract important information, facilitating efficient content browsing and search. Additionally, LMMs can perform well in applications such as virtual assistants and human-computer interaction. By processing both text and audio inputs simultaneously, they can provide more personalized and contextually relevant responses, enhancing user experience and interaction efficiency.

However, as of now, integrating different data types for processing requires a vast amount of training data and lengthy training times.

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