In consumer AI , what are Large Language Models (LLM's) and why all the buzz around them after Chat GPT and Bard?

LLM's are behind AI going massively mainstream in the past couple of years. But how do they work? What are their limitations? A primer to get started

Photo by Radek Grzybowski on Unsplash

In my day job, I work for Google. This will be the first in a series of essays where I will document my learning across a few specific topics under AI that are relevant to my career and hobbies. AI is complex and I am not a deep learning scientist or a machine learning ninja. My objective is just to understand the simplified real-world ramifications of this technology on the user side, since what AI goes on to become will have a profound impact on the work I do and the way I do it.

I will, for now. mostly focus on the user aspects of Generative AI, Large Language Models, Generative Pre Trained Transformers, Vector Databases, NLP and the basics of deep learning (sans the mathematics).

These are the technologies that power tools like Bard and Chat GPT.

Essay: What are LLMs (Large Language Models)

In its simplest form, LLM's are state-of-the-art language models trained on vast amounts of text data to understand & generate human-like text. 

These state-of-the-art models have undergone extensive training on massive amounts of text data, allowing them to acquire an impressive level of understanding and proficiency in generating human-like text. Through their immense neural networks and sophisticated algorithms, LLMs can analyze patterns within vast corpora consisting of books, articles, websites, social media posts—essentially anything written—and distil this knowledge into coherent responses that often feel remarkably natural. 

The innovation behind these AI-powered language models lies not only in their ability to comprehend complex linguistic nuances but also in their capacity for creative generation. With proper guidance and fine-tuning from researchers, these systems can produce highly engaging narratives, that can completely upend the way of work in many industries.

Why all the buzz around LLMs? 

These sophisticated models have sparked excitement due to their potential to bridge the gap between machine understanding and human conversation. LLMs represent a breakthrough in natural language processing technology by enabling machines to comprehend text at an unprecedented level of complexity. Unlike traditional rule-based systems that rely on predefined patterns or instructions, LLMs learn from massive amounts of data through advanced techniques such as deep learning. One key aspect contributing to the fascination with LLMs is their ability to generate coherent responses based on contextual cues within conversations. By leveraging vast knowledge repositories built into these models during training, they can provide insightful information while maintaining conversational flow—a quality previously challenging for AI-powered chatbots or virtual assistants attempting meaningful interactions. While still far from attaining true general AI capabilities—where machines possess complete human-like reasoning abilities—the progress made with LLMs is significant. Their capacity for nuanced comprehension allows them to grasp subtle nuances including sarcasm, analogy-making, and context-dependent meanings more effectively than previous iterations of language models.

But how do they work?

At their core, LLMs rely on complex deep learning architectures that harness the power of neural networks and train these LLMs with massive amounts of data so they can understand and generate human-like text. The architecture consists of multiple layers where each layer learns different patterns and features from the input data through a process called backpropagation. With such vast parameter sizes, LLMs have an incredible capacity to capture intricate relationships within language—allowing them to comprehend context, grammar rules, word associations, nuances in meaning—all while generating coherent responses tailored for specific tasks like translation or summarisation. By leveraging this immense computational firepower combined with extensive pre-training followed by fine-tuning on domain-specific datasets —LMMs become adept at understanding natural language inputs and generating meaningful outputs based on learned patterns.

The power of LLMs isn’t magic—it’s math!

They find patterns in data, and given enough data & compute, they can predict text incredibly well

During the training process, the model analyses the input data and calculates the probabilities of different words and sequences of words appearing together. It learns the statistical patterns, grammar rules, and common phrases in the data.

Once trained, the language model uses this learned information to generate text or make predictions. Given a prompt or partial sentence, the model calculates the probabilistic distribution of possible next words or sequences of words. It then selects the most likely options based on the patterns it has learned.

The power of LLMs lies in their ability to capture complex linguistic patterns and generate coherent and contextually relevant text. The more data and computational resources available for training, the better the model becomes at predicting text.

The key ingredient that sets them apart is an insatiable appetite for both high-quality training data and substantial computing capacity. More relevant examples fed into a language model during its training phase along with sufficient computation time dedicated to refining its predictions lead to increasingly exceptional performance levels. By employing statistical modelling approaches alongside deep neural network architecture known as Transformers—a type specifically designed for processing sequential data like text—these models can grasp complex linguistic nuances found within various languages seamlessly.

It’s important to note that while LLMs can generate impressive text, they don’t possess true understanding or consciousness. They operate purely based on statistical patterns and do not have a deep comprehension of the meaning behind the words they generate.

Think of LLMs as the next evolution of the search engine

In today's world, search engines are not just tools for finding information, but intelligent systems that can interpret and even generate it. Unlike traditional search engines that rely on keywords to fetch relevant results, LLMs leverage advanced Natural Language Processing techniques to understand context, semantics, and user intent. These sophisticated algorithms enable them to grasp complex queries with ease and provide more accurate responses by diving deep into vast amounts of data. But what sets LLMs apart is their ability to go beyond retrieving existing information—they have the potential to create new insights themselves. By analyzing patterns across texts or documents from various sources, they can generate original content tailored specifically to users' needs. However, as promising as this evolution may be in terms of convenience and efficiency at our fingertips—we must also recognize its implications on traditional search as it has been for decades and how that needs to evolve

How can traditional search evolve with the advent of LLM's?

Here are a few possible ways LLMs can help evolve traditional search engines:

• Help improve search engine algorithms by better understanding user queries and providing more accurate and relevant search results

• Assist in optimizing website content by providing insights into the language patterns and preferences of users, helping to create more engaging and informative content

• Aid in identifying and addressing search engine ranking factors, such as keyword usage, semantic search, and user intent, to improve website visibility and organic traffic

• Make search optimization more personalized and tailored to individual users, taking into account their preferences and search history

• Assist in optimizing voice search, allowing search engines to understand and respond to spoken queries more effectively

The challenges, however, that LLMs still face

LLMs have evolved massively, and though, they now possess vast amounts of knowledge, there is a key limitation they still need to surmount.

Brittleness.

While these models are incredibly knowledgeable on various subjects, they lack true understanding similar to what is exhibited by humans. Unlike human comprehension which involves complex cognitive processes such as context analysis, reasoning abilities and deeper connections between ideas or concepts, LLMs primarily rely on statistical patterns in data for generating responses. This means that while an LLM can provide information based on what it has learned from massive datasets, its responses might not always reflect genuine comprehension or grasp the nuances beneath the surface. It is also incapable of originality, ie, thinking of something new that it has not been taught. Therefore, although LLMs have made significant advancements towards mimicking certain aspects of human-like communication through machine learning algorithms trained on extensive datasets of text sources—they still fall short in capturing the essence of connectedness present within our understanding capabilities.

Limitations of Language Models (LLMs):

1. Lack of contextual understanding: LLMs struggle with understanding context beyond the immediate sentence or paragraph. They may not fully grasp the broader meaning or intent behind a conversation or document

2. Difficulty in handling ambiguous or contradictory information: LLMs find it challenging to navigate situations where there is ambiguity or conflicting information. They may provide inconsistent or inaccurate responses when faced with such scenarios

3. Vulnerability to adversarial attacks and biases: LLMs can be manipulated through adversarial attacks, where slight modifications to input can lead to significant changes in output. They are also susceptible to biases present in the training data, which can result in biased or unfair responses

4. Inability to reason or explain their decision-making process: LLMs generate responses based on patterns and associations learned from training data, but they cannot explain their reasoning or provide justifications for their answers

5. Limited ability to understand nuances, sarcasm, or humour: LLMs struggle with understanding subtle nuances, sarcasm, or humour in text. They may interpret such expressions literally or miss their intended meaning altogether

6. Tendency to generate plausible-sounding but incorrect or misleading information: LLMs can produce responses that sound plausible but are factually incorrect or misleading. This is because they primarily rely on statistical patterns in the training data rather than real-world knowledge verification.

Mainstream use cases

Clinical Hypothesis and Healthcare: In terms of diagnosis, LLMs can process vast amounts of medical literature and clinical data to provide clinicians with relevant information that aids them in making accurate diagnoses. By analyzing symptoms reported by patients and comparing them with a comprehensive database of cases, LLMs can suggest potential conditions or offer differential diagnostic insights to assist physicians

Education: Educators can create customised learning paths tailored specifically for each student based on their unique strengths, weaknesses, interests and preferred pace of learning. These systems utilise sophisticated algorithms and data analytics to analyse student performance across various assessments and activities. This enables teachers to gain valuable insights into each learner's progress while identifying areas where additional support may be necessary. Furthermore, LLMs provide opportunities for differentiated instruction by offering diverse resources such as interactive multimedia content modules, virtual simulations and real-time feedback mechanisms which enhance engagement with course material. Through these features, LMS platforms ensure that learners receive relevant educational materials aligned with their specific goals, making every step along their academic journey meaningful, purposeful, and enjoyable.

Business: Enhancing business workflows. Whether it be drafting documents or conducting intricate market analyses, businesses can leverage LLMs to streamline their operations with increased speed and accuracy. Enterprises can harness the power of LLMs to generate tailored contracts, proposals, reports or any other textual content effortlessly — saving precious time while maintaining professional quality standards.

The versatility of these AI-powered assistants allows them to adapt seamlessly across different domains within the business realm.

Content Creation: Writers and content creators can leverage LLMs to generate ideas, conduct research, or even draft entire articles by providing prompts or outlines. These models assist in enhancing productivity by offering quick suggestions as well as expansion of concepts. 

For writers seeking fresh ideas, LLMs offer a vast repository of texts from various genres and styles. They can be used to generate story plots or dialogue snippets that spark new creative directions. By leveraging LLMs' ability to understand context and language nuances, wordsmiths can infuse their work with more depth and originality. Designers can use these intelligent algorithms as well to provide innovative perspectives during the ideation process. With access to massive libraries filled with diverse visuals generated through machine learning techniques combined with human creativity—graphic designers now possess powerful resources at their fingertips.

Programming: Developers can use LLMs as virtual assistants when encountering coding challenges. By posing specific queries related to programming languages or debugging issues, developers can receive insightful guidance from these AI-powered tools that can help them solve problems more efficiently.

• LLMs can help programmers by providing code completion suggestions and auto-generating code snippets. They can understand the context of the code and suggest relevant code segments, saving programmers time and effort

• Assist in writing documentation and comments by generating descriptive text based on the provided code or context. This can help programmers explain their code more effectively

• Summarise code while extracting the main functionality or purpose of a code snippet. This can be helpful in understanding complex codebases or reviewing code

• Assist in bug detection and fixing by analysing code and providing suggestions to resolve common programming errors or improve code quality

• Aid in natural language processing tasks, such as extracting information from text or performing sentiment analysis, which are useful in various programming applications

• Help programmers stay up to date with the latest programming languages, frameworks, and libraries by providing relevant information and examples from online resources

• Support programmers in learning new programming concepts and techniques by answering questions, providing explanations, and offering examples

---