2. Large Language Models(LLMs)
• Large Language Models (LLMs) are machine learning models or
can be termed as deep learning algorithms, that can understand,
comprehend and generate human language text
• They work by analyzing massive data sets of language and they
are trained using huge data sets. The large amount of data sets
makes LLM models capable of understanding and generating
natural language and other types of content to perform a wide
range of tasks
• The LLM algorithm converted to a computer program is provided
with set of examples, so that the LLM can recognize and interpret
human language or other types of complex data.
3. • Though the LLMs are trained on data that has been gathered from the
Internet and other trusted resources, the output of the data totally
depends on the quality of the samples.
• The quality of the data impacts on how well LLMs will learn natural
language, then the developers can exploit the data to the extent that
LLM can perform as per expectations
• LLMs use machine learning techniques named deep learning to
understand how characters words, and sentences function together.
• Deep learning involves the probabilistic analysis of unstructured data,
which eventually enables the deep learning model to recognize
distinctions between pieces of content without human intervention.
• Large language models are also referred to as Neural Networks (NNs),
which are computing systems inspired by the human brain. These
neural networks work using a network of nodes that are layered, much
like neurons
4. • LLMs are then further trained and fine-tuned, to do the particular task
that the programmer wants them to do, such as interpreting and
understanding the questions and generating responses, or
translating text from one language to another.
• Apart from teaching human languages to Artificial Intelligence (AI)
applications, large language models can also be trained to perform a
variety of tasks like understanding protein structures, DNA research,
writing software code, online searching, Chatbots, customer service
and similar tasks for which LLM can be well trained.
• The LLMs work similarly to the human brain. First the human brain is
taught, trained and then it becomes capable of doing certain tasks in an
expected manner. In the same manner, large language models must be
pre-trained and then fine-tuned so that they can solve text classification,
question answering, document summarization, and text generation tasks.
5. Large Language Models Transformer
Model
• A transformer model is the most
common architecture of a large
language model. It consists of
an encoder and a decoder. A
transformer model processes
data by tokenizing the input,
then simultaneously conducting
mathematical equations to
discover relationships between
tokens.
Linear
6. • The encoder reads and processes the input text, transforming it
into a format that the model can understand. Imagine it as
absorbing a sentence and breaking it down into its essence.
• On the other side, the decoder takes this processed information
and steps through it to produce the output, like translating the
sentence into another language.
• This back-and-forth is what makes transformers so powerful for
tasks like translation, where understanding context and
generating accurate responses are key. This enables the computer
to see the patterns a human would see when the same query is given
as input.
7. Large Language Models Key Components
• Large language models are composed of multiple neural network
layers. Various neural networks, such as recurrent layers, feed
forward layers, embedding layers, and attention layers work
together to process the input text and generate output.
• The embedding layer creates embedding's from the input text that
captures the semantic and syntactic meaning of the input, so the
model can understand the context and relevance.
• The feed forward layer (FFN) of a large language model is made
up of multiple fully connected layers that transform the input
embedding's. While doing this process, these layers enable the
model to get insight into higher-level abstractions helps model to
understand the user's intent with the text input.
8. • The recurrent layer interprets the words in the input text in
sequence, that captures the relationship between words in a
sentence.
• The attention mechanism enables a language model to concentrate
on single part of the input text that is relevant to the task under
consideration. This layer allows the model to generate the most
accurate outputs.
9. Large Languages Model Types
1. Generic (raw) language model - This model predicts the next
word based on the language in the training data. This language
model performs information retrieval tasks.
2. Instruction-tuned language model - This model is trained to
predict responses to the instructions given in the input. This is
useful to perform sentiment analysis, or to generate text or code.
3. Dialog-tuned language model - This model is trained to have a
dialog by predicting the next response, like the chatbots.
10. Large Language Models Working
• LLMs operate by leveraging deep learning techniques and vast
amounts of textual data. These models are typically based on a
transformer architecture, like the generative pre-trained
transformer, which excels at handling sequential data like text input.
• A large language model is based on a transformer model and
works by receiving an input, encoding it, and then decoding it to
produce an output prediction. But before a large language model
can receive text input and generate an output prediction, it requires
training, so that it can fulfill general functions, and fine-tuning,
which enables it to perform specific tasks.
11. • Task 1 - Training - Large language models are pre-trained using
large textual datasets from sites like Wikipedia, GitHub, or others.
These datasets consist of trillions of words, and their quality will
affect the language model's performance. At this stage, the large
language model engages in unsupervised learning, meaning it
processes the datasets fed to it without specific instructions. During
this process, the LLM's Al algorithm can learn the meaning of
words, and of the relationships between words. It also learns to
distinguish words based on context.
• Task 2 - Fine-tuning- In order for a large language model to
perform a specific task, such as translation, it must be fine-tuned to
that particular activity. Fine-tuning optimizes the performance of
specific tasks.
12. • Task 3 - Prompt-tuning - This task is similar to a function to fine-
tuning, whereby it trains a model to perform a specific task
through few-shot prompting, or zero-shot prompting. A prompt
is an instruction given to an LL.M. Few-shot prompting teaches the
model to predict outputs through the use of examples
13. Large Language Models Use Cases/Applications
• Large language models are useful for business tasks that involve
following sub-tasks:
1. Text summarization.
2. Text translation.
3. Text and image generation.
4. Code writing and debugging.
5. Web search enabling search engines to respond to queries, to
assisting developers with writing code.
6. Customer service and sentiment analysis.
7. Virtual assistants/chatbots.
8. Text/Document classification.
14. 9. Automated document review and approval.
10. Knowledge base responses to answer queries.
11. Copywriting and technical writing.
12. Large language models have the ability to understand proteins,
molecules, DNA, and RNA. This helps to assist in the
development of vaccines, finding cures for illnesses.
13. LLMs are used in industries for customer service purposes such as
conversational Al, chatbots.
14. Marketing teams can use LLMs to perform sentiment analysis to
quickly generate campaign ideas or text as pitching examples.
15. From searching through massive textual datasets to generating
legalese, large language models are literally the assistants to
lawyers and the entire law community.
16. LLMs can support credit card companies in detecting fraud or
similar issues.
15. Large Language Models Benefits
1. Efficiency - LLMs can significantly improve the efficiency of
processes due to their ability to understand and process natural
language at a large scale with huge data processing.
2. Large set of applications - LLMs can be used for language
translation, sentence completion, sentiment analysis, question
answering, mathematical equations, and such tasks.
3. Cost reduction - With LLMs, tasks such as customer support, data
analysis, and others can be automated, thus reducing operational costs.
16. 4. Continuous improvement- Large language model performance is
continually improving because it grows when more data and
parameters are added. In other words, the more it learns, the better it
gets. Large language models can exhibit what is called "in-context
learning."
5. Data analysis- LLMs can analyze and interpret vast amounts of data
faster and more effectively than humanly possible, providing
businesses with valuable insights quickly.
6. Real time response to customer - LLM-based applications can
enhance customer interactions by offering personalized assistance and
real-time responses within stipulated time.
7. Scalability- LLMs can handle an increasing amount of work due to
their deep learning process
17. Large Language Models Limitations
• LLMs have various limitations and challenges that they faced:
1. Hallucinations - A hallucination is event when a LLM produces
an output that is false, or that does not match the user's intent. The
result can sometimes be what is referred to as a "hallucination."
2. Security - LLMs face important security risks when not managed
or surveilled properly. They can leak people's private information,
participate in phishing scams, and produce spam.
3. Ethical concerns and bias - LLMs are trained on vast amounts of
data from many sources, so they might reflect and reproduce the
biases present in those data sets. The data used to train language
models will affect the outputs a given model produces. If the data
represents a single demographic, or lacks diversity, the outputs
produced by the large language model will also lack diversity and
hence bias will be inherent
18. 4. Consent - LLMs are trained on trillions of datasets - some of
which might not have been obtained consensually or unknown to
owners. When scraping data from the internet, large language
models have been known to ignore copyright licenses, plagiarize
written content, and repurpose proprietary content without getting
permission from the original owners or artists.
5. Scaling- It is very critical to scale on time- and resource-
consuming factors so as to maintain large language models.
6. Deployment - Deploying large language models requires deep
learning, a transformer model, distributed software and hardware,
and overall technical expertise.
