LLMs: The Future of AI? Building Your Own Model

This blog is originally published on Signity Solutions and has been republished with permission.

Here, we will discover how to build a LLM from scratch. Why do you want to do such a thing, you ask? Well, LLMs are incredibly useful for untold applications, and by building one from scratch, you understand the underlying ML techniques and can customize the LLM to your specific needs.

Language plays a fundamental role in human communication, and in today's online era of ever-increasing data, it is inevitable to create tools to analyze, comprehend, and communicate coherently.

This is where LLM comes in the big picture.

A Large Language Model is an ML model that can do various Natural Language Processing tasks, from creating content to translating text from one language to another. The term "large" characterizes the number of parameters the language model can change during its learning period, and surprisingly, successful LLMs have billions of parameters.

Key Takeaways:

Comprehend everything about LLMs and their present state of the art.
Understand different types of LLMs and evaluate if it is a - fad or wham.
Discover the best ways to train LLMs from scratch and analyze them.

So, let's talk about it!

What is (LLM) Large Language Models?

In layman's terms, the "Large Language Model" is a trained deep-learning model that understands and produces content in a human-like manner. Behind the big stage, a large transformer model does wonders.

A Large Language Model is a deep-learning algorithm that does several Natural Language Processing tasks.

Furthermore, large learning models must be pre-trained and then fine-tuned to teach human language to solve text classification, text generation challenges, question answers, and document summarization. The potential of Top Large Language Models to solve diverse problems finds applications in fields ranging from finance and healthcare to entertainment., where these LLM models serve an array of NLP applications, like AI assistants, chatbots, translation, and so on.

Large Language Models consist of untold parameters, akin to memories the model gathers as it learns during training. You can consider these parameters as the model's knowledge bank.

A Quick Recap of the Transformer Model

In the year 2017, everything changed.

Vaswani announced (I would prefer the legendary) paper "Attention is All You Need," which used a novel architecture that they termed as "Transformer."

Nowadays, the transformer model is the most common architecture of a large language model. The transformer model processes data by tokenizing the input and conducting mathematical equations to identify relationships between tokens. This allows the computing system to see the pattern a human would notice if given the same query.

Besides, transformer models work with self-attention mechanisms, which allows the model to learn faster than conventional extended short-term memory models. And self-attention allows the transformer model to encapsulate different parts of the sequence, or the complete sentence, to create predictions.

All in all, transformer models played a significant role in natural language processing. As companies started leveraging this revolutionary technology and developing LLM models of their own, businesses and tech professionals alike must comprehend how this technology works. Understanding how these models handle natural language queries is especially crucial, enabling them to respond accurately to human questions and requests.

How Do You Evaluate Large Learning Models?

The Large Language Model evaluation can't be subjective. Instead, it has to be a logical process to evaluate the performance of LLMs.

Considering the evaluation in scenarios of classification or regression challenges, comparing actual tables and predicted labels helps understand how well the model performs. Often, we look at the confusion matrix for this. But what in the case of LLM? They generate text.

Don't worry! There are two approaches to evaluate LLMs - Intrinsic and Extrinsic.

1.) Intrinsic Methods

Conventional language models were evaluated using intrinsic methods like bits per character, perplexity, BLUE score, etc. These metric parameters track the performance on the language aspect, i.e., how good the model is at predicting the next word.

Perplexity: Perplexity is a measure of how well an LLM can predict the next word in a sequence. Lower perplexity indicates better performance.
BLEU score: The BLEU score is a measure of how similar the text generated by an LLM is to a reference text. A higher BLEU score indicates better performance.
Human evaluation: Human evaluation involves asking human judges to rate the quality of the text generated by an LLM. This can be achieved by using a variety of different assessments, like fluency, coherence, and relevance.

Moreover, it is equally important to note that no one-size-fits-all evaluation metric exists. Each metric has its own strengths and weaknesses. Therefore, it is essential to use a variety of different evaluation methods to get a wholesome picture of the LLM's performance.

Here are some additional considerations for evaluating LLMs:

Dataset Biasing: LLMs are trained on large datasets of text and code. If these datasets are biased, then the LLM will also be limited. It is essential to be aware of the potential for bias in the dataset and to take steps to mitigate it.
Safety: LLMs can be used to generate harmful content, such as hate speech and misinformation. It is essential to develop protection mechanisms to prevent LLMs from being used to create harmful content.
Transparency: It is essential to be transparent about the way that LLMs are trained and evaluated. This will help build trust in LLMs and ensure they are used responsibly.

2.) Extrinsic Methods

With advancements in LLMs nowadays, extrinsic methods are becoming the top pick for evaluating LLMs' performance. The suggested approach to evaluating LLMs is to look at their performance in different tasks like reasoning, problem-solving, computer science, mathematical problems, competitive exams, etc.

EleutherAI launched a framework termed Language Model Evaluation Harness to compare and evaluate LLM's performance. HuggingFace integrated the evaluation framework to weigh open-source LLMs created by the community.

This framework evaluates LLMs across four different datasets. The final score is an accumulation of scores from each dataset. Here are the parameters:

A12 Reasoning - This is a collection of science questions created for elementary school students.
MMLU - This is a comprehensive test that evaluates the multitask precision of a text model. It sheaths 57 different tasks, including subjects like U.S. history, math, law, and much more.
TruthfulQA - This test assesses a model's tendency to create accurate answers and skip generating false information commonly found online.
HellaSwag - This is a test that challenges state-of-the-art models to make common-sense inferences that are easy for humans, with 95% precision.

For more information and insight, read the complete blog here.