The term "LLM" has been generating a lot of buzz on social media, with mentions often carrying an air of mystery or awe. What are LLMs exactly, and in which areas are they making a significant impact? This blog post is an attempt to shed light on these questions.

For many new to AI, an introduction to LLM would have been courtesy of ChatGPT, which has the world in raptures and/or doubt, depending on which side of the debate you're on. ChatGPT is a popular large language model, but it is just one of many remarkable models out there. You may have even used one without realizing it, such as Google's BERT. BERT has been powering Google’s search engine since 2018. 

What Are LLMs?

LLMs or Large Language Models are AI systems trained on massive amounts of text, from which they have gleaned the intricate patterns and nuances of human language. Leveraging this knowledge, they can generate human-like text, a capability that makes them suitable for tasks such as content creation, translation, virtual agent, etc.

LLMs represent a significant milestone in natural language processing as they have broken through numerous preexisting limitations that once constrained language comprehension and generation.

Traditional language processing models relied heavily on predetermined rules or patterns to understand language. LLMs, on the other hand, consider the broader context of words and sentences. Again, unlike older models that produced robotic text, LLMs have the ability to generate diverse and imaginative content, ranging from articles to stories and beyond.

Beyond ChatGPT: An Overview of Different LLMs

Now let’s take a look at a few LLMs, their characteristics and capabilities. 

Llama 2

Llama 2 is an open-source language model developed by Meta. It is designed for both research and commercial use. It stands out from its predecessors with its expansive improvements, including a substantial increase in training data, doubling the context length, and specialized variants like Llama Chat and Code Llama. 

Llama 2 has a training dataset of 2 trillion tokens. It performs well across various benchmarks, excelling in reasoning, coding, and proficiency tasks. Like many deep learning models, the generalization capability and bias mitigation of the model are strongly influenced by the size, accuracy, and variety of the training dataset.


Dolly 2.0, led by the dolly-v2-12b model, is an instruction-focused language model from Databricks, Inc. While it may not be considered a cutting-edge model, Dolly 2.0 surprises with its high-quality instruction-following capabilities. Dolly 2.0 is also available in smaller sizes for various applications.

This model excels in following instructions effectively, drawing from a dataset of around 15,000 instruction/response pairs generated by Databricks employees across multiple domains, including classification, QA, and summarization. However, it has some limitations in handling complex prompts, programming tasks, factual accuracy, and creative tasks. 


The T5 (Text-to-Text Transfer Transformer) model introduces a new approach in the field of NLP by suggesting a universal text-to-text format for handling various NLP tasks. In this framework, both input and output are represented as text strings. This allows for a single model to undergo supervised training across a diverse array of NLP tasks, encompassing translation, classification, question answering, summarization, and even regression. 

Its flexibility extends to fine-tuning for specific downstream tasks, making it a versatile and highly effective tool for NLP practitioners and researchers alike. But like other models, T5 demands a substantial amount of computational resources and memory for its operation, posing a challenge for smaller companies or individual developers to harness its full potential effectively.


Whisper is an automatic speech recognition (ASR) system trained on an extensive dataset of 680,000 hours of multilingual and multitask supervised data. It understands spoken English at a level comparable to human performance, even in the presence of accents, background noise, and complex technical language.

This ASR model’s architecture utilizes an encoder-decoder Transformer for speech recognition tasks. It makes 50% fewer errors in zero-shot scenarios compared to other models. It handles both transcription and translation tasks in multiple languages. Its major limitation is that it is slow and expensive.

PaLM 2

PaLM 2, Google's advanced language model, performs various tasks, including advanced reasoning, multilingual translation, and coding, surpassing its predecessor, PaLM. It achieves this through compute-optimal scaling, an improved dataset mixture, and enhanced model architecture. 

PaLM 2 prioritizes responsible AI practices and evaluates for potential harms and biases. This model powers generative AI tools like Bard, the PaLM API, and MakerSuite, and enhances productivity and creativity in applications like Gmail and Docs. PaLM 2's massive size demands substantial computational resources and energy.


Generative Pre-trained Transformer-3,  developed by Open AI, learns from vast amounts of text data and can perform tasks like text completion, translation, and content generation with impressive fluency and coherence.

GPT-3 offers different model sizes to cater to various applications, making it a versatile choice for a wide range of natural language processing tasks. GPT-3 has limitations as it has been found to produce biased or inaccurate responses.


GPT-4 is one of the largest language models to date. It has the ability to fetch information from web pages via shared URLs. With an enhanced multilingual proficiency, GPT-4 can support 25 languages apart from English. It is a multimodal model, capable of handling both text and image data simultaneously.

Furthermore, GPT-4 offers increased "steerability," allowing users greater control over its responses through customizable personalities. However, like GPT -3, GPT-4 has also been criticized for producing biased or inaccurate responses and for being too computationally intensive for some applications.


StableLM is an open-source large language model launched by Stability AI, the same firm that came up with Stable Diffusion, the AI-based image generator. It is built on The Pile dataset, which contains 1.5 trillion tokens of content. The model has demonstrated its potential for handling complex scenarios in AI management. 

Large-scale datasets hosted by StableLM ensure reasonable outcomes in conversational tasks. StableLM base models can be freely inspected, used, and modified by developers for business or academic endeavors. As the company notes on its GitHub page, in the absence of finetuning and reinforcement learning, the model may generate offensive or irrelevant information.


BERT developed by Google in 2018 with 340 million parameters represents a significant milestone in NLP. The model is designed to understand the context and meaning of words by considering the words that come before and after them in a sentence. This bidirectional approach allows BERT to have a better understanding of language.

A widely adopted model, BERT has been fueling advancements in sentiment analysis, machine translation, etc. However, BERT can be computationally expensive and requires large amounts of training data. Lighter versions are also available, such as DistilBERT, MobileBERT, etc.


Alpaca is a transformer-based open-source LLM developed by Stanford. Like GPT, it is pre-trained on a massive amount of text data and can generate natural language text in response to a given prompt. It is optimized for generating informative and factually correct responses.

However, Alpaca has not been extensively tested in comparison to other LLMs, and its performance may vary depending on the quality and size of the training data. It is not available for commercial use and is currently being subjected to additional testing.


XLNet is a language model that uses a different pre-training objective than other models like GPT-3 and BERT. Instead of predicting the next word in a sentence, XLNet considers all possible permutations of words in the input sequence, making it good at handling ambiguous contexts. 

XLNet has shown impressive performance in several benchmark NLP tasks, including text classification, language translation, and question-answering. However, like other large language models, XLNet requires significant computational resources and training data.


This is a variant of BERT developed by Facebook AI. It uses an architecture similar to BERT but, with improved pre-training techniques and data augmentation strategies, it does better on several benchmark NLP tasks. 

RoBERTa has been used in a range of applications, including text classification, named entity recognition, and language modeling. Like other models, RoBERTa too can be computationally expensive and requires significant training data.

Common Use Cases of LLMs

LLM models are applied to various NLP tasks based on their individual strengths, training, and specific requirements.

Language Translation

Language translation is one of the most practical applications of large language models. With the help of LLMs, machines can learn to translate multiple languages, including some that may not have been translated before. 

An LLM undertakes language translation by first being trained on vast amounts of text data in multiple languages to learn the patterns and structures of each. This training data includes parallel texts—texts in one language that have been translated into another. Once trained, the model is given a sentence in the source language, and it uses its knowledge of language patterns to generate the equivalent sentence in the target language.

Customer Chatbots

Large Language Models play a significant role in making chatbots, which have become a ubiquitous presence in various industries, sound more like humans. By analyzing language patterns and detecting intents, these models can help chatbots better understand what users are saying and provide appropriate responses. Some companies have implemented LLMs in their chatbots to add a human touch to automated interactions with customers.

The models are trained on a large dataset of conversational data, including questions and answers, which helps them learn the patterns of language used in conversations. They use this knowledge to analyze incoming messages, identify keywords and context, and generate responses based on the patterns learned. The more data the model is trained on, the better it becomes at understanding and responding to user messages.

Content Generation

The best-known use case of LLMs is perhaps content generation with ChatGPT being its most visible example. LLMs like ChatGPT use deep learning algorithms to generate text based on patterns they have learned. Training on different content types, such as news articles, essays, or fiction equips them with the knowledge of specific language patterns. 

A recent job ad for a content writer position by a tech giant said: “The applicant must be proficient in ChatGPT.”  That is some pause for thought!

While there are concerns about the ethical implications of using AI-generated content, there are many benefits to automated content generation, such as cutting down labor-intensive tasks and expediting content production.

Personalized Marketing

LLMs can support personalized marketing by analyzing user behavior, preferences, and past interactions with a company's marketing channels and then generating personalized marketing messages.

Marketing and sales teams can utilize these models to dynamically generate targeted content, craft hyper-personalized campaigns, and deliver real-time customer support. By harnessing the capabilities of large language models for personalized marketing, companies can drive better customer engagement and conversion.

Sentiment Analysis

Sentiment analysis enables companies to strategically shape their marketing based on customer sentiment. LLMs can offer them a more accurate analysis by virtue of their heightened contextual understanding.

A large language model uses NLP techniques to identify the sentiments expressed in social media posts or customer reviews. The model is trained on a large dataset of labeled text, where each piece of text is classified as positive, negative, or neutral. The model learns the patterns and structures of language associated with each sentiment and uses this knowledge to analyze new pieces of text and classify them accordingly.

Limitations of Large Language Models

The above-discussed potential apart, there are also several challenges and limitations to LLMs. One of the significant challenges is the massive amount of data required to train them. Large Language Models require a vast corpus of text data to train, making it challenging for smaller organizations to develop their models.

Another challenge is the computational resources required to train and fine-tune them. Training a Large Language Model requires a significant amount of computational power, which can be expensive and time-consuming.

Also, LLMs are yet to develop the capability to understand the context of text data fully. They can generate text that is grammatically correct but may lack the context and nuance that only humans can understand. As of now, there is no consensus on LLMs reaching the same cognitive and creative level as humans.

LLM-Based Service Offerings

With constant training and upgrades on a large scale, LLMs may be heading toward a bright future. It is very likely that LLMs could be a game changer in many industries. And when it does, technology service providers will have a big role to play as they can offer a range of LLM solutions and allied services to enterprises, including:

  • Fine-tuning LLMs for specific domains, such as legal, healthcare, or finance, to maximize the performance of LLMs for their specific use cases.
  • Leveraging API services for rapid access to the latest updates in LLM technology while saving on time, resources, and infrastructure maintenance.
  • Deploying and building inference engines using open-source LLMs for organizations that do not want to use cloud-based services.

On a Closing Note...

As with any technology, responsible and ethical use is important! As for the gloomy predictions about AI-related technology replacing humans, the perspective of Fei-Fei Li, Co-Director of Stanford Institute for Human-Centered AI, is pertinent: "AI will not replace humans; it will augment humans. We deserve AI that complements our strengths and makes up for our weaknesses." She is essentially advocating for the development of AI systems that work collaboratively with humans and amplify our capabilities.

The future is exciting, and we can't wait to see what innovations lie ahead!