Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.
LLMs have become a household name thanks to the role they have played in bringing generative AI to the forefront of the public interest, as well as the point on which organizations are focusing to adopt artificial intelligence across numerous business functions and use cases.
Outside of the enterprise context, it may seem like LLMs have arrived out of the blue along with new developments in generative AI. However, many companies, including IBM, have spent years implementing LLMs at different levels to enhance their natural language understanding (NLU) and natural language processing (NLP) capabilities. This has occurred alongside advances in machine learning, machine learning models, algorithms, neural networks and the transformer models that provide the architecture for these AI systems.
LLMs are a class of foundation models, which are trained on enormous amounts of data to provide the foundational capabilities needed to drive multiple use cases and applications, as well as resolve a multitude of tasks. This is in stark contrast to the idea of building and training domain specific models for each of these use cases individually, which is prohibitive under many criteria (most importantly cost and infrastructure), stifles synergies and can even lead to inferior performance.
LLMs represent a significant breakthrough in NLP and artificial intelligence, and are easily accessible to the public through interfaces like Open AI’s Chat GPT-3 and GPT-4, which have garnered the support of Microsoft. Other examples include Meta’s Llama models and Google’s bidirectional encoder representations from transformers (BERT/RoBERTa) and PaLM models. IBM has also recently launched its Granite model series on watsonx.ai, which has become the generative AI backbone for other IBM products like watsonx Assistant and watsonx Orchestrate.
In a nutshell, LLMs are designed to understand and generate text like a human, in addition to other forms of content, based on the vast amount of data used to train them. They have the ability to infer from context, generate coherent and contextually relevant responses, translate to languages other than English, summarize text, answer questions (general conversation and FAQs) and even assist in creative writing or code generation tasks.
They are able to do this thanks to billions of parameters that enable them to capture intricate patterns in language and perform a wide array of language-related tasks. LLMs are revolutionizing applications in various fields, from chatbots and virtual assistants to content generation, research assistance and language translation.
As they continue to evolve and improve, LLMs are poised to reshape the way we interact with technology and access information, making them a pivotal part of the modern digital landscape.
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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. LLMs consist of multiple layers of neural networks, each with parameters that can be fine-tuned during training, which are enhanced further by a numerous layer known as the attention mechanism, which dials in on specific parts of data sets.
During the training process, these models learn to predict the next word in a sentence based on the context provided by the preceding words. The model does this through attributing a probability score to the recurrence of words that have been tokenized— broken down into smaller sequences of characters. These tokens are then transformed into embeddings, which are numeric representations of this context.
To ensure accuracy, this process involves training the LLM on a massive corpora of text (in the billions of pages), allowing it to learn grammar, semantics and conceptual relationships through zero-shot and self-supervised learning. Once trained on this training data, LLMs can generate text by autonomously predicting the next word based on the input they receive, and drawing on the patterns and knowledge they've acquired. The result is coherent and contextually relevant language generation that can be harnessed for a wide range of NLU and content generation tasks.
Model performance can also be increased through prompt engineering, prompt-tuning, fine-tuning and other tactics like reinforcement learning with human feedback (RLHF) to remove the biases, hateful speech and factually incorrect answers known as “hallucinations” that are often unwanted byproducts of training on so much unstructured data. This is one of the most important aspects of ensuring enterprise-grade LLMs are ready for use and do not expose organizations to unwanted liability, or cause damage to their reputation.
LLMs are redefining an increasing number of business processes and have proven their versatility across a myriad of use cases and tasks in various industries. They augment conversational AI in chatbots and virtual assistants (like IBM watsonx Assistant and Google’s BARD) to enhance the interactions that underpin excellence in customer care, providing context-aware responses that mimic interactions with human agents.
LLMs also excel in content generation, automating content creation for blog articles, marketing or sales materials and other writing tasks. In research and academia, they aid in summarizing and extracting information from vast datasets, accelerating knowledge discovery. LLMs also play a vital role in language translation, breaking down language barriers by providing accurate and contextually relevant translations. They can even be used to write code, or “translate” between programming languages.
Moreover, they contribute to accessibility by assisting individuals with disabilities, including text-to-speech applications and generating content in accessible formats. From healthcare to finance, LLMs are transforming industries by streamlining processes, improving customer experiences and enabling more efficient and data-driven decision making.
Most excitingly, all of these capabilities are easy to access, in some cases literally an API integration away.
Here is a list of some of the most important areas where LLMs benefit organizations:
Text generation: language generation abilities, such as writing emails, blog posts or other mid-to-long form content in response to prompts that can be refined and polished. An excellent example is retrieval-augmented generation (RAG).
Content summarization: summarize long articles, news stories, research reports, corporate documentation and even customer history into thorough texts tailored in length to the output format.
AI assistants: chatbots that answer customer queries, perform backend tasks and provide detailed information in natural language as a part of an integrated, self-serve customer care solution.
Code generation: assists developers in building applications, finding errors in code and uncovering security issues in multiple programming languages, even “translating” between them.
Sentiment analysis: analyze text to determine the customer’s tone in order understand customer feedback at scale and aid in brand reputation management.
Language translation: provides wider coverage to organizations across languages and geographies with fluent translations and multilingual capabilities.
LLMs stand to impact every industry, from finance to insurance, human resources to healthcare and beyond, by automating customer self-service, accelerating response times on an increasing number of tasks as well as providing greater accuracy, enhanced routing and intelligent context gathering.
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Watsonx.ai provides access to open-source models from Hugging Face, third party models as well as IBM’s family of pre-trained models. The Granite model series, for example, uses a decoder architecture to support a variety of generative AI tasks targeted for enterprise use cases.
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Developed by IBM Research, the Granite models use a “Decoder” architecture, which is what underpins the ability of today’s large language models to predict the next word in a sequence.
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