LLMs Overview & Insight

Overview of LLMs

These days in IT industry lots of Organizations are investing their resources in discussions, researches, courses and implementations of tools and technologies to evolve Artificial Intelligence for enhancing the productivity and efficiency through software tools. “Machine Learning”, the major branch of AI is always at core of almost every solution requiring machine automation and automated learning. From the very basic ML models, industry is now evolved so much and reached to a level where anyone can leverage the outcomes to further improvise the results using more robust and well-trained models which are available with paid licenses and open source for usage.

Industry is talking about lot of AI technologies like Natural Language Processing (NLP), Deep Learning, Large Language Models (LLMs), Generative AI (GenAI), Retrieval Augmented Generation (RAG), Computer Visions, Robotics, Speech Recognition, etc. Here, in this blog we will describe about Large Language Models (LLMs) and will try to explain all the basics for easier understanding about it.

Large Language Models (LLMs) are AI models in use for NLP tasks which understands and generates human-like text. These models are trained on huge datasets with vast number of parameters, often consist of billions of words, to understand complex syntax, patterns, variations and grammar of languages. Now, you may have the understanding what “Large” means in LLMs. The “Large” in LLMs generally refers to the number of parameters it uses to train the model. Any model can consist of millions or billions of parameters for model training based on the use case and dataset.

LLMs are subset of NLP which help computers to understand, analyze and generate human languages for multiple solutions including Question and Answer, Language Recognition, Translation, Summarization, Content Rewriting, Classification, Semantic & Sentiment Analysis, etc.

Insights of LLMs

LLMs are nothing but a language expert, trained on huge number of datasets and parameters to produce the human-like content. Let’s straight jump on to the details how it works.

1. Transformer Model – Within the inner core, LLMs are extensive transformer model and it helps the machines to understand different languages. It acts like a language detective who analyzes every word in a sentence considering its importance and order with other words too. At a very high level, it helps LLMs to understand the language and pattern to do all type of operations on language.
2. Attention Mechanism – Imagine a case when someone is reading a sentence/phrase and encounters a word which is unknown to him. Then one might look to previous or subsequent sentences for context to understand the meaning. Attention serves a similar purpose for transformer models, allowing focus on salient portions of an input to better comprehend context. This enhances performance on tasks such as translation or question answering.
3. Pretraining – Pretraining massive language models is comparable to guide them through the rudiments of language using a wealth of examples from various literature works and online resources. It’s like granting them access to an extensive library for exploration before engaging in specific tasks such as composing articles or responding to queries. This approach contributes significantly to their grasp of language nuances and enables them to execute their tasks with heightened proficiency.
4. Foundational Models – Imagine a foundation model as a grand cookbook that becomes a language expert by devouring vast amounts of text. Subsequently other models can leverage this knowledge as a starting point tweaking it slightly to perform various language tasks such as translation or summarization. Constructing a foundation model entails pretraining it on a vast dataset.
5. Fine-tunning – Fine tuning a large language model is required when someone wants to customize it for a specific job. Think of it as tailoring a glove to perfectly fit your hand using the right tools. Models comes with a variety of options to fine tune them to achieve the desired goal. One can adapt and fine tune the pretrained language model to perform a specific function such as summarizing articles or creating imaginative stories making it more accurate and efficient for the assigned role.
6. Few Shot Learning – Few shot learning is like instructing an LLM to undertake a new task with a few instances similar to mastering a novel game after limited exposure. Rather than relying on copious examples the LLM adeptly absorbs knowledge from a small sample enhancing its versatility and adaptability to unfamiliar challenges.
7. Zero Shot Learning – Learning through a zero shot approach is like to being thrown into a quiz on an unfamiliar topic. You must rely on your existing knowledge to navigate through uncharted territory much like making well informed guesses without any prior preparation. In the realm of zero shot learning the language model can approach unknown tasks without specific training by leveraging its knowledge on related ideas. It mirrors the process of utilizing your existing knowledge to decode something new even when its entirely unfamiliar.
8. Instructor Tunning – Tweaking instructions is like adjusting the spices in a recipe to suit your taste. When it comes to language models adjusting instructions allows you to fine tune the model guidance to better align with the specific task. It’s like providing the model with a clearer path to follow so that it can execute its duties more efficiently.
9. Context Length – Context length is like how many words your brain can handle before hitting the mental pause button. If you are a quick thinker a sentence or two might be your limit before needing a breather. Much like brains, computers also have their limits to read and analyze the content for generating correct context out of it. In the tech realm context length dictates that how many words a model can chew in a single mental bite.
10. Prompt Engineering – Picture yourself guiding a robot on how to prepare a sandwich. You wouldn’t simply say “make a salad” and leave it to figure out. Instead, you would provide detailed steps like ­ take cucumber, onion and tomato, peal them one by one, slice them nicely and pour spice on it. This process known as prompt engineering involves providing precise instructions to assist a large language model to achieve the desired task. By furnishing clear prompts or questions rather than vague directives you steer the model towards producing the intended outcome effectively.
11. Hallucination – In LLMs it is a scenario where model predicts an output which far away from reality or not related to input content. This occurs because the model often produces fluent and coherent text based on patterns it has learned, regardless of the accuracy or relevance of the information. Hallucination may lead to deceptive or irrational content, particularly in situations where the model lacks sufficient context or knowledge to make well informed decisions.
12. RAG – By giving LLMs access to outside data, RAG improves the accuracy of their responses and also helps in decreasing hallucinations. Using the concept of retrieval and generation, RAG helps the model to provide the relevant output by understanding the context of input, avoid errors and handle new points all the more successfully. The model’s overall performance is enhanced by this collaboration, which also addresses some of its shortcomings. QnA, content generation, conversational systems, educational tools and information retrieval are few of the areas in which RAG can assist. In a variety of applications, RAG enhances the diversity and accuracy of generated text by incorporating external knowledge.