You’ll want to know this about LLMs
Large language models (LLMs) are AI models that use deep learning techniques, “invented by Google,” a subset of neural networks known as transformers. LLMs use transformers to perform natural language processing (NLP) tasks like language translation, text classification, sentiment analysis, text generation, and question-answering. LLMs are trained with a massive amount of datasets from a wide array of sources. Their immense size characterizes them – some of the most successful LLMs have hundreds of billions of parameters.
LLMs are Critically Important
LLMs are trained on hundreds of billions of parameters and are used to tackle the obstacles of interacting with machines in a human-like manner.
LLMs bridge the gap between human communication and machine understanding. LLMs are found in healthcare and science, where they are used for tasks like gene expression and protein design. DNA language models (genomic or nucleotide language models) can also be used to identify statistical patterns in DNA sequences. LLMs can be life savers for customer service support functions like AI-Chatbots and Conversational AI.
How Do LLMs Work?
LLMs are trained on billions of pieces of data, both unstructured and structured data before going through the transformer neural network process.
After pre-training on a large corpus of text, the model can be fine-tuned on specific tasks by training it on a smaller dataset related to that task. LLM training is primarily done through unsupervised, semi-supervised, or self-supervised learning.LLMs are transformer neural network deep learning algorithms. They learn context and understanding through sequential data analysis.
Transformer was introduced an “Attention Is All You Need” by Google a 2017 white paper. The transformer model uses an encoder-decoder structure; it encodes the input and decodes it to produce an output prediction. Below is an example of it:
Multi-head attention is a key component of Transformer architecture. It allows the model to weigh the importance of different tokens (e.g. text/characters) in the input when making predictions for a particular token. The “multi-head” aspect allows the model to learn different relationships between tokens at different positions and levels of abstraction.
There are Four Common LLM Types
The common types of LLMs are Language Representation, Multimodal, Zero-Shot, and Domain-Specific. The details of these LLM are as follows:
1. Language Representation Model
Many NLP applications are built on language representation models (LRM) designed to understand and generate human language. Examples of such models include GPT (Generative Pre-trained Transformer) models, BERT (Bidirectional Encoder Representations from Transformers), and RoBERTa. These models are pre-trained on massive text corpora and can be fine-tuned for specific tasks like text classification and language generation.
2Gemini and Clip Multimodal Model LLMs
LLMs were initially designed for text content. However, multimodal models work with both text and image data. These models are designed to understand and generate content across different modalities. For instance, OpenAI’s CLIP is a multimodal model that can associate text with images and vice versa, making it useful for tasks like image captioning and text-based image retrieval.
Google’s DeepMind new Gemini looks amazing, and Integrating Google’s Gemini into Google Cloud and enhancing the capabilities of Google cloud services and makes them AI-powered.
3.Zero-Shot Model
Zero-shot models are known for their ability to perform tasks without specific training data. These models can generalize and make predictions or generate text for tasks they have never seen before. GPT-3 is an example of a zero-shot model – it can answer questions, translate languages, and perform various tasks with minimal fine-tuning.
4. Domain-Specific LLMs
Pre-trained language representation models may not always perform optimally for specific tasks or domains. Fine-tuned models have undergone additional training on domain-specific data to improve their performance in particular areas. For example, a GPT-3 model could be fine-tuned on medical data to create a domain-specific medical chatbot or assist in medical diagnosis.
Example LLMs
Aside from ChatGPT and Gemini the MM-LLM, there are several other LLMs.
· BERT: Bidirectional Encoder Representations from Transformers is a deep learning technique for NLP developed by Google Brain. BERT can be used to filter spam emails and improve the accuracy of the Smart Reply feature.
· Generative pre-trained transformers (GPT): Developed by OpenAI, GPT is one of the best-known large language models. It has undergone different iterations, including GPT-3 and GPT-4. The model can generate text, translate languages and answer your questions in an informative way.
· LLaMA: Large Language Model Meta AI was publicly released in February 2023, with four model sizes: 7, 13, 33, and 65 billion parameters. Meta AI released LLaMA 2 in July 2023, available in three versions, including 7B, 13B, and 70B parameters.
· Pathways Language Model (PaLM): PaLM is a 540-billion parameter transformer-based LLM developed by Google AI. As of this writing, PaLM 2 LLM is currently being used for Google’s latest version of Google Bard.
· XLNet: XLNet is an autoregressive Transformer that combines the bidirectional capability of BERT and the autoregressive technology of Transformer-XL to improve the language modeling task. It was developed by Google Brain and Carnegie Mellon University researchers in 2019.
Seven Interesting LLM Use Cases
LLMs are still relatively new and under development, but they can assist users with various tasks in various fields, including customer service, healthcare, education, and entertainment. Here is a small set of common uses of LLMs:
- Code and text generation: Language models can generate code snippets, write product descriptions, create marketing content, or even draft emails.
- Language translation: LLMs can generate natural-sounding translations across multiple languages, enabling businesses to communicate with partners and customers in different languages.
- Question answering: Companies can use LLMs in customer support chatbots and virtual assistants to provide instant responses to user queries without human intervention.
- Education and training: The technology can generate personalized quizzes, provide explanations, and give feedback based on the learner’s responses.
- Customer service: LLM is one of the underlying technologies for AI-powered chatbots used by companies to automate customer service in their organization.
- Legal research and analysis: Language models can assist legal professionals in researching and analyzing case laws, statutes, and legal documents.
- Scientific research and discovery: LLMs contribute to scientific research by helping scientists and researchers analyze and process large volumes of scientific literature and data.
Key Advantages of LLMs
LLMs offer an enormous potential productivity boost for organizations that generate large volumes of data. The benefits LLMs deliver to such companies:
Enhanced Question-Answering Capabilities
LLMs can also be described as an answer-generation machine. LLMs are so good at generating accurate responses to user queries so much that experts had to weigh in to convince users that generative AIs will not replace the Google search engine.
Increased Efficiency
LLMs ability to understand human language makes them suitable for completing repetitive or laborious tasks. For context, LLMs can generate human-like text much faster than humans, making it advantageous for tasks like content creation, writing code or summarizing large amounts of information.
Transfer Learning
LLMs serve professionals across various industries — they can be fine-tuned across various tasks, enabling the model to be trained on one task and then repurposed for different tasks with minimal additional training.
Few-Shot or Zero-Shot Learning
LLMs can perform tasks with minimal training examples or without any training at all. They can generalize from existing data to infer patterns and make predictions in new domains.
LLM Issues Today
LLMs also have known drawbacks that can affect the quality of their results.
LLM Ethical Concerns
The use of LLMs raises ethical concerns regarding potential misuse or malicious applications. There is a risk of generating harmful or offensive content, deep fakes, or impersonations that can be used for fraud or manipulation.
Lack Of Common-Sense Reasoning
Despite their impressive language capabilities, large language models often struggle with common sense reasoning. For humans, common sense is inherent – it’s part of our natural instinctive quality. But for LLMs, common sense is not in fact common, as they can produce responses that are factually incorrect or lack context, leading to misleading or nonsensical outputs.
LLM Performance Depends on Training Data
The performance and accuracy of LLMs rely on the quality and representativeness of the training data. LLMs are only as good as their training data, meaning models trained with biased or low-quality data will most certainly produce questionable results. This is a huge potential problem as it can cause significant damage, especially in sensitive disciplines where accuracy is critical, such as legal, medical, or financial applications.
Top LLM Tools Used in 2023-2024
The following LLMs are commonly used you’ll encounter in the generative AI landscape:
· PyTorch: LLMs can be fine-tuned using deep learning frameworks like PyTorch. For example, OpenAI’s GPT can be fine-tuned using PyTorch.
· Hugging Face Transformers: The Hugging Face Transformers library is an open-source library providing pre-trained models for NLP tasks. It supports models like GPT-2, GPT-3, BERT, and many others.
· OpenAI API: The company (OpenAI) provides an API that lets developers interact with their LLMs. Users can make requests to the API to generate text, answer questions, and perform language translation tasks.
· spaCy: spaCy is a library for advanced natural language processing in Python. While it may not directly handle LLM, it’s commonly used for various NLP tasks such as linguistically motivated tokenization, part-of-speech tagging, named entity recognition, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, and entity linking.
The Future of LLMs
LLMs will learn from themselves and mature in all aspects. Future evolutions may be better for bias detection, increased transparency, and making them a more trusted and reliable resource for critical industries like healthcare, finance, and education. There’ll be greater variety in domain specific LLMs of, giving companies more options to choose from. And, customized LLMs will abound. And become more easy to use in much more specific things and contexts. This will allow each piece of AI software to be fine-tuned to be faster and far more efficient and productive.
It’s highly probable that LLMs will become commodities, and hence considerably less expensive, allowing SMBs and even individuals to leverage the power and potential of LLMs.
LLM are democratizing AI
LLMs are becoming the great equalizer and have revolutionized industries by automating language-related processes. However, LLM deployment has its ethical concerns, like biases from their training data, anticipated misuse, and training privacy concerns. Leveraging LLM potential with responsible and sustainable development is critical for potential LLM benefits.
The advent of 3GPP’s 6G will find a native AI end-to-end, and this will reshape how we interact with technology, from chatbots and content generation to translation to emotional-sensing and metaverse environments. Its going to be ever more exciting for those of us with curious open minds.
