LARGE LANGUAGE MODEL

What are LLMs?

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 process (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 model, 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.

How large language models work??

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.

LLM use cases?

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.?

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.??