What is LLM (Large Language Model)?

What is LLM (Large Language Model)

LLM stands for Large Language Model and refers to a large language model. By learning the vast amount of text data on the internet, LLMs learn word connections and contextual patterns. As the name "large-scale" suggests, it is characterized by an order of magnitude larger "data volume" and "computational amount" than conventional AI. With this vast knowledge, LLMs can understand user instructions and perform various language tasks, such as answering questions, summarizing sentences, translating, and conversing, with human-like fluency.

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Differences between LLMs (Large Language Models) and Generative AI

The difference between LLM and generative AI is the relationship between "whole" and "part"

LLMs are a model of generative AI and are an important technology that plays a core role in generative AI.

●Generative AI:

A general term for AI technology that can "generate" completely new content such as text, images, audio, and program code on its own. There are various types for different purposes, such as text generation AI, image generation AI, and music generation AI.

●What is LLM: 

LLM is a model of generative AI, and enables the generation of human-like natural language, images, audio, video, and program code. By learning vast amounts of data, it understands the relationship among words, images, audio and other type of data and generates contents, summarizes, translates natural sentences and also generate steam video with voice and audio.

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How LLMs Work

LLMs can generate natural-like human-like sentences by working with the following mechanisms: 

1. Pre-Training:

First, let them train using vast amounts of text data. LLMs load vast amounts of text data from around the world, including websites, books, and papers on the internet, as "textbooks." At this stage, LLMs are learning the pattern of word-to-word connections, such as which word is most likely to come after a certain word. For example, after the sentence "what is the weather tomorrow", you will learn countless statistical relationships, such as "sunny" and "rainy".

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2. Fine tuning:

Pre-learning involves training students based on common data on the internet, which can lead to inaccurate information and un-contextually appropriate answers. Therefore, we will additionally train and adjust the data of the target business area so that we can respond more naturally and helpfully to humans. For example, by having additional data specialized for supply chain operations trained, LLMs can be created that are more useful for the supply chain. This fine-tuning allows the LLM to generate answers that better understand the intent of the user's question.

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3. Inference:

When a user inputs a question or instruction (prompt), the LLM goes live. From the vast amount of knowledge learned, we probabilistically and quickly predict the word connections that best match the given instructions, and construct sentences while selecting the next word one by one. LLMs generate surprisingly natural sentences by repeatedly making probabilistic predictions based on "massive data learning" and "human fine-tuning." It is thanks to this reasoning that it is able to generate sentences according to instructions.

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Common use cases of LLMs

By devising the way instructions (prompts) are given, LLMs can dramatically streamline various "thinking tasks" such as generating ideas, writing sentences, gathering information, and analyzing information. Whether it's automating simple tasks to focus on core tasks, or getting new ideas as a wall of ideas, the possibilities are endless.

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Business scenes) You can expect a dramatic increase in productivity and the creation of new ideas.

Dramatically speed up document creation: In summarizing meeting minutes and conducting research, we accurately extract key points from vast amounts of information, speeding up time-consuming document creation. 
Smooth communication: With high-precision and natural translations that capture technical terms and subtle nuances, it significantly reduces the burden of writing and realizes multilingual communication.
Stimulate creativity: As an excellent wall-to-wall opponent, we encourage idea generation and accelerate creative work such as content creation by generating multiple original drafts such as social media posts.
Streamline software development: Streamline all processes of software development, from natural language code generation to debugging, dramatically shortening development cycles.

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Daily life scenes) The use of it to enrich our lives is also expanding.

Learning support: You can explain difficult technical terms and have them talk to you when learning a foreign language.
Information search: Instead of comparing multiple websites yourself, you can simply ask "What is the difference between 〇〇 and △△" to summarize the main points and answer them. 
Creative partners: Get tips for creative activities, such as writing a novel synopsis, planning a travel plan, or creating a menu.

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Main features of the LLM provided by Fujitsu

Fujitsu offers Takane, an enterprise large language model that can be used securely in a private environment, focusing on the needs of enterprise users who need to be customizable and secure.

Takane was jointly developed with Cohere as a specialized LLM with the world's highest level of Japanese processing capabilities and was launched in September 2024.

It can be used in a private environment for customers who require a high level of security, such as finance, government offices, and R&D departments, and can safely use sensitive business data for generative AI.

It is an LLM that can fine-tune with company/organization-specific data, which is difficult for general-purpose LLMs. You can use your trusted business data to customize it for your business. We can also optimize our customers' operations using world-class RAG technology.

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Frequently asked questions

Q. What is the difference between LLM and SLM?

A. LLMs (Large Language Models) are general-purpose models that can provide highly accurate responses based on vast amounts of knowledge. On the other hand, SLM (Small Language Model) is an AI model that is lighter and more efficient to operate offline.

Q. What is the difference between LLM and RAG?

A. LLM (Large Language Model) is an AI model that generates sentences based on pre-trained knowledge from vast amounts of text data. On the other hand, RAG (Retrieval-Augmented Generation) complements the capabilities of LLMs by searching for necessary information from external databases and generating more accurate and up-to-date responses based on the contents. By combining LLMs with RAG, you can get more specific and reliable answers.

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Related Sites

Takane

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What is LLM (Large Language Model)?