What is an LLM? - Definition, principles, Models, Applications

One of the world's largest venture capital (VC) firms, A report released by Sequoia Capital reveals that enterprise adoption of LLMs (large language models) has risen dramatically since the ChatGPT craze. The percentage of companies surveyed by Sequoia Capital that created apps with the new LLM jumped from 15% to 65% in just two months. The applications of AI varied, and most companies are using publicly available APIs rather than developing their own LLMs.

As the promise of generative AI gains traction, so does interest in LLMs. In this article, we'll cover what LLMs are, how they work, use cases, and how to build them.

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What is a Large Language Model (LLM)?

Definition.

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A language model (LM) is a type of artificial intelligence model trained to understand and produce human language. They learn patterns, structures, and relationships within a given language and have been used for narrow AI tasks such as text translation. The quality of a language model depends on its size, the amount and variety of data trained on it, and the Depends on the complexity of the learning algorithm.

If so, a Large Language Model (LLM) is a large language model. LLM은 Deep Learning algorithms and statistical modeling to identify the most likely candidates for We use it to perform Natural Language Processing (NLP) tasks. These models are trained on large amounts of linguistic data beforehand to understand and generate sentence structure, grammar, meaning, and more.

For example, in the problem of predicting the next word in a given context, LLM can determine the similarity and context between words in a sentence to generate the next word. These tasks are utilized in a variety of NLP tasks, including machine translation, text summarization, automatic writing, and question answering. LLM has a variety of models, such as Generative Pre-trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). These models have hundreds of billions of parameters. Recently, using large amounts of training data and large model architectures, There is a growing interest in achieving more sophisticated language understanding and generation.

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NLP vs. LLM

NLP and LLM are related concepts, but they are not the same thing.

NLP is a field of artificial intelligence that focuses on understanding and processing human language. NLP aims to develop techniques for computers to understand and analyze natural language text. NLP is utilized for a variety of tasks such as sentence parsing, text categorization, machine translation, question and answer systems, sentiment analysis, and more.

LLM, on the other hand, refers to large language models trained using large datasets. It uses deep learning techniques and statistical modeling to perform natural language processing tasks.

In other words, NLP is an umbrella term for the field of natural language processing, which focuses on the art of understanding and processing text. The LLM is a subset of NLP, which is It focuses on using language models trained on large amounts of linguistic data to perform specific NLP tasks. NLP is a broader concept, and LLM is one form within it that refers to specific approaches and models.

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Why is the Large Language Model (LLM) important?

Industry applicability of giant language models

Large-scale language models are suitable for languages or scenarios that require different types of communication. This has broadened the scope of AI applications across industries and across the board, and is expected to bring new horizons to research, creativity, and productivity. Below are some of the many ways in which the LLM can be utilized.

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Retailers and other service providers are building on LLM to create chatbots, AI assistants, and more The quality of our customer service.
Search engines that apply LLM can provide more human-like responses.
Life science researchers can train LLMs to understand proteins, molecules, DNA, and RNA.
With an LLM, developers can write software code and even teach robots physical tasks.
Marketers train LLMs to cluster customer feedback or requests, and even segment products into categories based on product descriptions.
Use LLM to summarize the Earning call and Log important meetings. Credit card companies can also use it to Detect anomalies or analyze possible fraud to help protect consumers.
An LLM can help you with legal interpretation, paperwork, and more.

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How LLM and foundational models impact the business environment

LLMs like ChatGPT are now so natural that they can barely pass the Turing test. As a result, LLMs are being used in a variety of fields and industries, like the examples above. But how are LLMs actually impacting the business world?

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Automating tasks: LLMs will take over many tasks currently performed by humans and Automation. Employees can focus on more strategic and creative work.
Improve customer service: You can use LLM to create chatbots that can answer customer questions and resolve issues 24/7.
Generate creative content: LLM Can be used to generate creative content.
Make better business decisions: LLM can be used to analyze data and forecast future trends. This enables companies to make better decisions about products, marketing, and investments.

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Foundation models are versatile ML models that train on very large data sets, and once trained, these models are used on standard consumer-grade equipment. Foundation models can do things that make up a large part of your business operations. For example, creating images or writing code. These pre-trained models can be customized for specific tasks or uses.

This model has a small number of variables and limited functionality. It is primarily used for early experimentation, proof of concept, and prototyping. Traditionally, companies had to pay a lot of money to build an LLM on their own. But now, with the underlying model, it's possible to take an early form of LLM and apply it to your business. Now open-source many of the underlying models, allowing companies to streamline costs and processes before building a model fit for purpose.

LLM and foundational models are not just about digitizing everyday experiences or environments; they are about taking almost any knowledge available online and turning it into a model that can effectively solve real-world problems. It's not about creating a simulation that looks like reality on the outside, but rather a living, interactive simulation. By building an LLM on top of an underlying model that we've trained on our domain knowledge, we've created a customized solution for our enterprise.

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How the Large Language Model (LLM) works

Types of language models

The language model is a work in progress. Before we dive into how it works, let's break down the four stages of development.

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SLM (Small Language Model): Focuses on learning a limited amount of textual data to understand local context across tasks. Despite their small size, SLMs are lightweight and fast to run.
Neural Language Model (NLM): NLMs provide more accurate performance than traditional statistical-based language models. These models are primarily used for a variety of NLP tasks, including word embedding, sentence completion, and machine translation.
PLM (Pretrained Language Model): PLMs are large datasets that pre-learning, which is then applied to various NLP tasks via transfer learning. Major models like BERT and GPT belong to this PLM.

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Engineers are using PLM, can be scaling improve model capacity in downstream operations. Many studies have shown that while training much larger PLMs, Exploring performance limits. It turned out that these large PLMs, unlike their smaller counterparts, were remarkably capable of solving a series of complex tasks. For example, GPT-3 had the ability to learn from situations and solve one-off tasks, whereas GPT-2 did not. The research community began to use the term "large language model" (LLM) for these large PLMs, meaning that LLMs are the current state of the art and final stage of development for language models.

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Frequently used terms in the LLM

Below are definitions of words frequently used in articles and papers that discuss LLM. Knowing them beforehand can help you understand the LLM.

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Word embedding: A technique that represents words as high-dimensional vectors to capture similarity and relationships between each word.
Attention mechanisms: Techniques for weighting different parts of an input sequence so that the model can focus on important information.
Transformer: A neural network model with an encoder and decoder structure based on the attention mechanism, excellent for processing sequences of different lengths.
Fine-tuning LLMs: The process of further training a large pre-trained language model to apply to a specific task.
Prompt engineering: The process of structuring questions or commands that are entered into a model to improve the performance of the model.
Bias: The tendency of a model to pick up imbalances or false patterns in the training data, resulting in results that do not match the reality of the real world.
Interpretability: The ability to overcome the complexity of an LLM and understand and explain the results and decisions of AI systems.

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How LLM works and why

Unlocking the Power of Large Language Models

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Now let's dive into how LLM works. The way LLM learns languages utilizes the principles of deep learning. You can think of LLM as a transfer learning model that has been pre-trained with a large amount of deep learning.

LLM is a deep learning model that finds the most natural sequence of words in a sentence. Using deep learning techniques, it is able to recognize words and phrases in a sentence and associate them to determine their linguistic meaning. In doing so, LLM does not follow specific rules such as grammar rules or dictionary meanings of words, but rather learns about their frequency and grammatical characteristics to generate contextually correct sentences. In other words, it can predict the next word in a sentence given the previous words, or it can recognize the works by predicting the middle word.

These artificial neural network-based language models can be trained on large amounts of data to produce natural, human-like sentences. Training LLMs is most often done by feeding large amounts of text data into a machine learning algorithm, which typically involves preprocessing such as tokenization to separate the string data, and then training using models such as BERT, GPT, GPT-2, GPT-3, and T5.

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Pictured above is the GPT model, which uses only the Decoder part of Google's published language model Transformer. GPT is more not only does it understand human intentions at a granular level, it can give appropriate answers and even knows how to speak like a human.

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LLM Core Skills

The LLM has been around for a long time, and a number of important techniques have emerged over the course of its development that have greatly enhanced its capabilities. Here's a brief overview of the important skills that enable LLMs to be competent learners.

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Scaling

Scaling up means further improving the performance of a language model by utilizing larger datasets and computing resources. Larger datasets allow the model to acquire more linguistic information and make more accurate predictions. Typically, LLMs scale up in two ways. The first is by increasing the size of the model, which requires more computational power and memory. Secondly, Data augmentation is a way to extend your model. Data augmentation is a way to create more data by transforming existing data, so that the model is able to capture various Help you acquire language patterns.

While LLM's model extensions have the advantage of improving performance to make it applicable to a wide range of NLP tasks, the computing resources and increased computational complexity. In addition, the quality of the pre-training data plays a key role in achieving good performance, so when scaling the pre-training corpus, we recommend that you Data collection and cleaning strategy is important to consider.

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Learning

Distributed training algorithms are required to learn the network parameters of LLM, where various parallel strategies are jointly utilized. To support distributed learning, several optimizations frameworks are arised. Optimization tricks like restarting and mixed precision training to overcome training loss spikes also play a key role in training stability and model performance. More recently, GPT-4 has shown that large models can perform as well as much smaller models in Specialized infrastructure and optimization methods that can reliably predict are on the development agenda.

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Derive abilities

With prior training on a large corpus, LLMs can can do a variety of things. However, there's no way to explicitly tell LLM how far it can go, so you'll need to come up with appropriate task instructions or learning strategies for specific situations. For example, chain-of-thinking prompts can be used in Intermediate inference steps have been found to be useful for solving complex inference tasks. Research has shown that performing instructional tuning on LLMs can improve the generalizability of LLMs to unseen tasks.

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Alignment Tuning

Alignment tuning uses alignment information between input and reference sentences to train a model. While traditional fine-tuning trains a model based on a dataset of input sentences and their corresponding labels, alignment tuning also uses alignment information between input and reference sentences. Alignment tuning can also be used in conjunction with fine-tuning.

Alignment tuning can be useful in tasks like language translation. For example, suppose you have the reference sentences "I eat apples" and "I eat an apple". Using alignment information, you can map "I" and "I" and "an apple" to each other and place them in the appropriate positions in the input sentence. With this alignment information, the model can better understand the meaning and grammatical structure of the sentence and provide more natural translation results.

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Tool Operations

In essence, LLMs are trained on a large corpus of plain text, which can lead to poor performance on tasks that are not well represented in textual form. They are also limited by their pre-training data, which cannot capture the latest information. To address these issues, recent theories have focused on using external tools to compensate for LLMs' shortcomings.

Recently, we enabled a mechanism for ChatGPT to use external plugins, which became the "eyes and ears" of LLM, extending its capacity far and wide.

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Key models and history

History

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The beginnings of LLM began in the 1960s. It started with a simple chatbot program and grew into a large model like ChatGPT today. Below are some of the key events in LLM's history and their descriptions.

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Eliza (Joseph Weizenbaum, 1960s): Uses pattern recognition to convert user input into questions, and generates responses based on a predefined set of rules.
LSTM (1997): Generate deeper, more complex neural networks to process larger amounts of data.
CoreNLP (2010): A set of tools and algorithms for complex NLP tasks such as sentiment analysis and named NTT recognition.
Google Brain (2011): Helping NLP systems better understand the context of words
Transformer (2017): Enabled the creation of larger, more sophisticated LLM models, and became the predecessor to GPT-3, which paved the way for AI-based applications.

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We also recently released a user-friendly frameworks and tools have been developed. Based on this, the evolution of LLM is still a work in progress.

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Featured Models

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The core models of LLMs that have gained the most traction in recent years include

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GPT-3.5 (OpenAI): Slight performance and stability improvements over GPT-3, and leverages extensive training data to improve language understanding and generation.
GPT-4 (OpenAI): The successor to GPT-3, with larger model sizes and more sophisticated language understanding and generation capabilities than its predecessor.
PaLM 2 (Google): A language model that uses pre-trained automatic metrics to evaluate performance on various NLP tasks such as machine translation, summarization, and question answering.
LLaMA: A task-oriented language model evaluation benchmark developed by Language Model Benchmark (LLaMA), which is used to evaluate and compare the performance of language models, including various natural language processing tasks.
Cohere: A platform of pre-trained language models that can be utilized in business applications. It applies to a variety of language tasks, including sentence generation, question answering, and summarization, and supports custom model development and deployment.

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Large Language Model (LLM) Use cases

Startup LLM Use Cases

In fact, many startups are building LLMs into their business based on a foundation model. If you look at the LLM applications of different companies, you will see that they have used different models and tuned them to match the color of their business. Their evaluation of the foundation model they choose is based on cost, performance, and accuracy. Most companies still tend to trust OpenAI's models. However, as the models improve and more granular or industry-specific models start to emerge, we'll see more diversity. Here, we look at how the startup ecosystem is leveraging LLM.

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Yoodli

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Use LLM to provide AI-powered speech coaching in the form of text-based feedback to users
Provide a summary of your speech that your audience will understand and suggestions for brevity, paraphrasing, and follow-up questions to prepare for

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Compose AI

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Automate the typing process to generate text, correct sentences, or enable autocomplete
Get ideas for stories, blog posts, website copy, research topics, and more, and reply to messages or emails.

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Speak

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Engage language learners in open-ended conversations
Provide contextualized feedback on how to speak more like a native speaker

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Seekout

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NTT extraction and text summarization, code summarization, content generation, semantic search, embedding, and code generation
Support talent acquisition and talent management

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Coda

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A work assistant to help you build and edit tables intelligently
Create categories, data, and content for meeting summaries, action item extraction, and automated workflows

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Examples of LLM implementation in Korea

While the GPT model is gaining momentum globally, South Korea is also making its mark on the world stage with its own technology. Let's take a look at how they built their own LLM.

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Huggingface's Open LLM Leaderboard is an authoritative ranking of more than 500 open models from around the world competing on average scores on metrics such as reasoning and common sense, language comprehension synthesis, and hallucination avoidance. Recently, a generative AI model developed by Upstage achieved a score of 72.3 on Huggingface's Open LLM Leaderboard, surpassing the performance of GPT-3.5.

Last month, Upstage's 30B (30 billion) parameter model scored an average of 67, overtaking Meta's LLaMA 2's 70B (70 billion) model released on the same day. Upstage has since fine-tuned its LLaMA 2 model with more data to regain the top spot in the world.

Upstage developed the first Korean natural language understanding (NLU) evaluation dataset, KLUE, and won four titles at the ICDAR OCR World Competition. Upstage also operates AskUp, Korea's leading multimodal generative AI service, which has grown to 1.3 million subscribers and has brought together Upstage's technology assets, including know-how in prompt engineering and fine-tuning.

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Things to watch out for when deploying LLM in production

How Prior Learning Data Affects LLM

Unlike smaller PLMs, LLMs typically massive computational resources, so pre-training is multiple iterations is close to impossible. Therefore, it is important to build a well-prepared training corpus before training the LLM. How will the quality and distribution of the pre-training training corpus affect the performance of LLM?

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Quality of training data
The quality of your pre-training data affects how well your model can understand and represent different aspects of language. Quality data should consist of natural-looking sentences that reflect grammar, meaning, and context. It should also include data from a variety of topics and domains so that the model can respond well to different problems. With quality data, your model will be able to generate and understand more accurate and meaningful sentences.
Distribution of prior training data
The distribution of training data affects how well a model can handle different aspects of a language when dealing with real-world problems. If the distribution of the training data is similar to the data in a real-world application, the model will be better able to learn the language patterns and domain features of that field and make more accurate predictions. For example, a model used in the medical field would benefit from being pre-trained with medical-related text data.
Amount of pre-training data
Existing research has shown that larger LLM parameter sizes require It turns out we need more data. The good news is that a similar scaling law is observed for data size as for model performance. Recently, LLaMA has shown that smaller models can achieve good performance with more data and longer training. Therefore, researchers should It emphasizes the need to pay more attention to the amount of high-quality data.

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Typically, pre-training data from different domains or scenarios have different linguistic characteristics or semantic knowledge. By pre-training with a mixture of text data from different sources, LLM can not only acquire a wide range of knowledge, but also have a strong generalization ability. Of course, when blending different sources, it's important to note that downstream tasks may need to You need to carefully determine the distribution of your pre-training data so as not to compromise model performance.

In addition, excessive data training on a particular domain can affect the LLM's ability to generalize to other domains. Therefore, researchers need to carefully determine the proportion of data from other domains in the pre-training corpus. This will allow them to develop LLMs that better fulfill their specific needs.

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What companies should watch out for when adopting a giant language model

While the benefits of AI are enormous, it can also have other side effects if adopted haphazardly, which is why it's important to be aware of the following and approach it strategically.

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Deploying cloud applications

While it's great to be able to deploy applications at the speed of business demand, there's a growing concern that some organizations are stretching application development to reckless levels. The work required to build and deploy a system is surprisingly short. However, many enterprises are finding that the You're not thinking enough about the overall role. Even if you've created an application because it's strategically necessary, it's possible that it's redundant down the road. Allowing this confusion to persist can be detrimental to your organization's future, as it increases management costs.

To overcome this, organizations need to have a clear purpose and strategy before adopting AI. Businesses need to know what problems they want to solve or What value do you want to create You need to set goals. By defining your goals and creating a strategy, you can guide your AI adoption.

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System scaling issues

Beyond Words: Large Language Models Expand AI's Horizon

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It's important that AI models are constantly improving and keeping up with the latest technological trends. Put processes in place to evaluate and improve the performance of your models, and keep an eye on AI research and development trends so you can apply the latest techniques.

However, supporting rapidly scaling AI systems requires a greater amount of computing and storage resources than in the past, which means that building AI systems requires adequate computing resources and infrastructure. Organizations must be able to commit sufficient resources to build and maintain the infrastructure. 또한, Your AI team needs to be properly augmented and staffed to operate effectively.

In addition, the quality of AI models is highly dependent on the quality of data, so data quality management is important. You need to check the accuracy, completeness, consistency, etc. of your data and pay attention to privacy. You need to have a proper approach and policy for privacy and data ethics. Therefore, the tuning process of pre-trained models becomes even more important. Below, we will discuss LLM's tuning methods: fine tuning and prompt tuning.

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Fine Tuning and Prompt Tuning

Leveraging LLMs on your domain-specific knowledge base | by Michiel De Koninck | ML6team

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Fine-tuning and prompt tuning are both methods for tuning a pre-trained language model to a specific task. While both methodologies are popular ways to tune LLMs, there are some differences. Let's compare their definitions and characteristics in detail here.

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Fine-tuning

Fine tuning is a method of retraining a pre-trained language model with additional task data across the entire language model. It uses the pre-trained model as the initial weights and retrains the model with additional training data for a specific task.

By retraining some or all of the model parameters, you can get a model that is highly task-specific.
You might need a large amount of additional job data.
In general, fine-tuning for new tasks can take a long time.
Increases the likelihood of making predictions that are specific to a particular task.

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Prompt tuning

Prompt tuning adds or modifies specific structured prompts to the input text so that the Tune the behavior of the model. Experiment with the best prompt configuration for your specific task, and manipulate the model output to get the results you want.

Fix the parameters of the pre-trained model, and tweak the prompt configuration to get the right results for your specific task.
You can reduce the burden of data acquisition by not having to do any additional work on your existing data.
It's less task-specific and more flexible for different tasks.
Experimentation with initial settings and prompt configurations is required, and there may be performance differences between settings.

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Fine tuning involves retraining the model itself to a specific task, while prompt tuning involves tuning the model's behavior by adjusting the structure of the input. Both methods should be chosen based on their respective strengths and weaknesses and the context in which they are applicable.

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Conclusion: Lightweighting models and improving training data quality are critical for business applications of LLM

Large LLMs require a lot of parameters and computational resources. Model lightweighting reduces model size and computational demands, enabling faster inference speed. It can also overcome limitations that make it difficult to deploy and use, such as device constraints, bandwidth, and storage. Lightweight models have the advantage of overcoming these constraints and being easy to deploy and use on a variety of platforms and environments.

If you're going through the fine-tuning and prompt tuning of a traditional LLM and you don't get the right results, you're wasting a lot of resources. On the other hand, LLMs that have become lighter and more affordable to run have the ability to tune their models in many ways, making them much more business-valid.

With the recent proliferation of open source models, you have more choices. While it's difficult to find an accurate way to benchmark LLMs, it's becoming easier to switch between them. Projects like Open LLM and FastChat make it easier to connect different models with different APIs and interfaces. Lightweight models allow us to stitch together layers and, in some cases, even run multiple models in parallel.

In addition, specific tuning know-how based on high-quality training data is becoming increasingly important to apply LLM to business. LLMs need to attempt to solve a user's challenge or answer a question in the most concise and efficient way possible, and it's important to provide users with accurate content instead of manipulated information. Quality and feedback data on large corpus datasets trained by LLMs are emerging as key competencies in order for LLMs to accurately define and measure user intent, as well as to be honest models.

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Reference.

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