What is a Large Language Model (LLM)? How does it work? Everything you need to know explained by Digimagg

A large language model (LLM) is an artificial intelligence algorithm crafted to comprehend and produce human-like language. Through extensive training with vast datasets and advanced learning methods, LLMs acquire the ability to comprehend the semantics and context of words. This proficiency allows AI chatbots to engage in dialogues with users and aids AI text-generation tools in tasks such as writing and summarization.

What is a Large Language Model?

Large language models (LLMs) are machine learning algorithms utilizing deep learning methods and extensive training data to comprehend and produce natural language. Their capacity to comprehend the significance and context of words and sentences empowers LLMs to excel in activities like text generation, language translation, and content summarization.

LLMs operate by taking in an input, such as a prompt or question, and utilizing sophisticated neural networks to predict the next logical word repeatedly, ultimately generating output that is coherent. Achieving this, LLMs heavily rely on vast amounts of data, typically incorporating at least a billion parameters, which are variables within a trained model enabling it to generate new content through inference. A higher number of parameters generally indicates a model with a more intricate and detailed comprehension of language, enhancing its performance across various tasks.

Present-day LLMs are the culmination of years of advancement in natural language processing and artificial intelligence. Accessible through interfaces such as OpenAI’s ChatGPT and Google’s Gemini, these models serve as potent tools for automating language-related tasks, fundamentally altering the way we interact, work, and create.

How do Large Language Models work?

In summary, the functioning of LLMs involves (1) receiving an input such as a command or query, (2) applying insights acquired from extensive training data, and (3) utilizing advanced neural networks to precisely forecast and produce outputs that are contextually appropriate.

The data

To achieve this, these models must undergo training using petabytes of textual data. Generally, this data is unstructured and has been collected from the internet with minimal cleaning or labeling. The dataset may encompass a variety of sources such as Wikipedia pages, books, social media threads, and news articles, amounting to trillions of words that serve as examples for grammar, spelling, and semantics.

The process of Training

Next comes the training phase, during which the model learns to forecast the subsequent word in a sentence based on the context provided by the preceding words. LLMs typically rely on transformer neural networks, denoted by the "T" in the GPT language models, which excel at processing sequential data such as text inputs. This architecture enables an LLM to discern connections between words by assigning a probability score to tokenized strings of words, meaning they are broken down into smaller sequences of characters and represented numerically.

Mikayel Harutyunyan, the Chief Marketing Officer of AI firm Activeloop, likens this process to that of a detective, where one must attribute varying degrees of significance to different clues and comprehend their interrelations to decipher the broader meaning. Similarly, the transformer model architecture assigns weights to specific characters, words, and phrases to aid the LLM in recognizing relationships between particular words or concepts, thus comprehending the overall message.

"If you input the phrase 'I will,' then it might predict something like 'I will survive,' 'I will always love you,' 'I will remember you,'" Harutyunyan explained to Built In. "The algorithm essentially endeavors to estimate which word would best suit the given text."

Self-taught Learning  

Training occurs through unsupervised learning, where the model independently learns the rules and structure of a given language based on its training data. Gradually, it becomes more proficient at discerning patterns and relationships within the data autonomously.

According to Vinod Iyengar, the Vice President of Product for AI company ThirdAI, "You don’t have to teach [LLMs] how to solve the problem, all you have to do is show them enough samples of correct and wrong answers, and the model usually picks it up. It understands the internal logic of how to solve the problem. These models are able to understand the internal structure of the language — the concepts — and they’re able to start making sense."

The output

In due course, the LLM reaches a stage where it comprehends the command or query provided by a user and produces a coherent and contextually appropriate response. This capability can be applied to various text-generation tasks.

Types of Large Language Models

Numerous variations of large language models exist, each possessing unique capabilities tailored for specific applications.

Multimodal model

Initially, LLMs were primarily optimized for text processing. However, multimodal models have advanced capabilities allowing them to process images, videos, and even audio through intricate algorithms and neural networks. "They integrate information from different sources to comprehend and produce content that combines these modalities," Sheth explained.

For instance, Sheth elaborated, "You could input both text and an image to a multimodal LLM, and it could generate a descriptive caption for the image, considering both the visual content and any textual context provided."

Zero-shot Learning model

Zero-shot learning models possess the capability to comprehend and execute tasks they have never encountered previously. They do not require specific examples or training for each new task; instead, they utilize their generalized understanding of language to deduce solutions instantly.

For instance, Beerud Sheth, the CEO of conversational AI company Gupshup, illustrated, "If you have a zero-shot LLM and you provide it with a prompt like, 'Translate the following English text into French: The weather is beautiful today,' the model can generate the translation without ever having been trained specifically on translation tasks."

Fine-tuned model

Fine-tuned models are essentially zero-shot learning models that have undergone additional training using domain-specific data to enhance their performance in a particular task or their proficiency in a specific subject area. Fine-tuning is a supervised learning process, necessitating a dataset of labeled examples to enable the model to more precisely identify the concept.

For instance, if you desire a model to provide more accurate medical diagnoses, it must undergo fine-tuning on a vast dataset of medical records. Similarly, if you seek a model capable of generating marketing content aligned with a particular company's brand, it should be trained using that company's data.

Large Language model applications

Large language models find utility across diverse industries and are applicable to a wide array of use cases. Below are some of the most common applications of this technology.

Conversational AI

LLMs empower AI assistants to engage in conversations with users in a manner that is more natural and fluent compared to earlier generations of chatbots. Through fine-tuning, they can also be customized to suit a specific company or purpose, be it customer support or financial assistance.

Text generation

LLMs have the capability to produce text on nearly any subject, whether it's an Instagram caption, blog post, or mystery novel. Additionally, these models excel at what Iyengar refers to as "style transfer," enabling them to imitate specific voices and tones. For instance, a text generator could craft a pancake recipe in the style of William Shakespeare or compose a marketing email with the tone of a Gen Z girl.

Code generation

LLMs serve as a valuable resource for developers in tasks such as coding, error identification in existing code, and even translation between various programming languages. Additionally, they can provide answers to coding-related inquiries in simple language.

Content Retrieval and Summarization 

LLMs demonstrate proficiency in condensing and retrieving crucial details from extensive documents. They adeptly grasp the context, extract essential concepts, and produce succinct summaries that encapsulate the essence of the original content, sparing individuals the need to read the entire document themselves.

For instance, a lawyer can employ an LLM to condense lengthy contracts or extract vital information from extensive evidence during the discovery phase. This technology is also leveraged in search engines, where the model generates straightforward responses to users' search queries on platforms like Google and Bing.

Language translation

LLMs excel at swiftly and precisely translating language across various forms of text, ranging from social media posts to product descriptions or entire documents. Moreover, a model can undergo fine-tuning to specialize in a specific subject matter or geographic region, enabling it to not only convey literal meanings in translations but also capture jargon, slang, and cultural nuances accurately.

Rewards of Large Language Models

Large language models have emerged as a prominent area in technology due to their numerous benefits. Let's delve into some of these advantages below.

LLMS are always improving

Large language models have the capacity to undergo continuous learning and enhancement with the introduction of new data. As these models encounter fresh information, they can dynamically adjust and refine their comprehension of evolving contexts and linguistic changes, thereby enhancing their performance progressively.

LLMS have seemingly endless applications

Due to their versatility and capacity for ongoing refinement, large language models appear to have limitless potential applications. Whether it's composing music lyrics or assisting in drug discovery and development, LLMs are being utilized in diverse fields. Furthermore, as the technology progresses, the boundaries of what these models can achieve are consistently expanding, offering innovative solutions across various domains of life.

LLMS can speed up time-consuming tasks

Normally, LLMs produce instantaneous responses, accomplishing tasks that would typically require humans hours, days, or even weeks in mere seconds. These models can efficiently analyze extensive documents or datasets and autonomously derive valuable insights from them. For instance, they can generate 100 distinct marketing emails (complete with subject lines) in response to a single-sentence prompt. As a result, LLMs streamline repetitive, time-intensive tasks, allowing humans to allocate more time to tackle more intricate and strategic pursuits.

LLMS are Versatile and Customizable

LLMs are widely recognized for their adaptability. They excel in various tasks, ranging from drafting business proposals to translating complete documents. Their proficiency in comprehending and generating natural language allows for fine-tuning and customization to specific applications and industries. This flexibility enables organizations or individuals to harness these models and tailor them to suit their distinct requirements.

Hurdles Faced by Large Language Models

That being acknowledged, LLMs are not flawless. Similar to any technology, they pose a number of challenges and drawbacks.

LLMS tend to be Biased

When an LLM is provided with training data, it absorbs the biases inherent in that data, resulting in biased outputs that can significantly impact the individuals who interact with them. Given that data typically mirrors the prejudices prevalent in society, often presenting distorted and incomplete portrayals of people and their experiences, models constructed on such foundations inevitably reflect and potentially amplify these imperfections. Consequently, this could result in offensive or inaccurate outputs at best and instances of AI-driven discrimination at worst.

LLMS can generate inaccurate responses 

LLMs frequently encounter difficulties with common-sense reasoning and accuracy, often leading to the generation of responses that are incorrect or misleading—a phenomenon referred to as AI hallucination. What's even more concerning is the lack of clarity when the model makes mistakes. Due to their design, LLMs present information in articulate, grammatically correct statements, making it effortless to accept their outputs as factual. However, it's crucial to recognize that language models primarily function as sophisticated engines for predicting the next word.

"They're essentially attempting to forecast which word or token would statistically be the most accurate," explained Harutyunyan from Activeloop. "While they might produce something that appears valid, it may not necessarily be true."

LLMS spark plagiarism concerns

The utilization of copyrighted material in the training data of LLMs is currently considered acceptable, albeit controversial. This practice has ignited a broader discussion, leading to lawsuits and debates involving news organizations, authors, and other creatives. Concerns have been raised regarding the ethical and legal implications, including issues of intellectual property rights, plagiarism, and the interpretation of fair use doctrine. Despite these discussions, the U.S. Copyright Office has made it clear that AI-generated work is ineligible for copyright protection.

LLMS contribute to environmental concerns 

The environmental impact of LLMs stands out as a significant concern on a global scale. Training these deep learning models requires substantial computational resources, resulting in a considerable carbon and water footprint.

According to a 2019 study, the process of training a single model can emit over 626,000 pounds of carbon dioxide, which is nearly five times the lifetime emissions of an average American car, including its manufacturing. Another research paper from 2023 revealed that training the GPT-3 language model necessitated the use of 700,000 liters of fresh water per day in Microsoft's data centers. As these models grow in size and usage, their environmental impact continues to escalate.

While AI has proven beneficial in combating climate change, efforts are underway to mitigate the water and carbon footprints associated with LLMs. However, the dual nature of AI's impact prompts researchers, companies, and users to confront the ethical considerations surrounding the future utilization of this technology.

LLMS’ outputs aren't always explainable 

Addressing challenges such as hallucinations, bias, and plagiarism in the future won't be straightforward, given the inherent complexity of understanding why a language model produces a specific output. Even AI experts, who possess a deep understanding of these algorithms and their intricate mathematical frameworks, struggle to pinpoint the exact mechanisms behind a model's response.

"With 100 billion parameters interacting simultaneously, it becomes incredibly challenging to isolate which parameters are influencing a particular output," explained Iyengar from ThirdAI.