A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. The term generative AI also is closely connected with LLMs, which are, in fact, a type of generative AI that has been specifically architected to help generate text-based content.
An LLM is the evolution of the language model concept in AI that dramatically expands the data used for training and inference. In turn, it provides a massive increase in the capabilities of the AI model. While there isn't a universally accepted figure for how large the data set for training needs to be, an LLM typically has at least one billion or more parameters. Parameters are a machine learning term for the variables present in the model on which it was trained that can be used to infer new content.
Modern LLMs emerged in 2017 and use transformer neural networks, commonly referred to as transformers. With a large number of parameters and the transformer model, LLMs are able to understand and generate accurate responses rapidly, which makes the AI technology broadly applicable across many different domains.
LLMs have become increasingly popular because they have broad applicability for a range of NLP tasks, including the following:
* Text generation. The ability to generate text on any topic that the LLM has been trained on is a primary use case.
* Translation. For LLMs trained on multiple languages, the ability to translate from one language to another is a common feature.
* Content summary. Summarizing blocks or multiple pages of text is a useful function of LLMs.
* Rewriting content. Rewriting a section of text is another capability.
* Classification and categorization. An LLM is able to classify and categorize content.
* Sentiment analysis. Most LLMs can be used for sentiment analysis to help users to better understand the intent of a piece of content or a particular response.
* Conversational AI and chatbots. LLMs can enable a conversation with a user in a way that is typically more natural than older generations of AI technologies.
Among the most common uses for conversational AI is with a chatbot, which can exist in any number of different forms where a user interacts in a query-and-response model. One of the most widely used LLM-based AI chatbots is ChatGPT, which is based on OpenAI's GPT-3 model.
There are numerous advantages that LLMs provide to organizations and users:
* Extensibility and adaptability. LLMs can serve as a foundation for customized use cases. Additional training on top of an LLM can create a finely tuned model for an organization's specific needs.
* Flexibility. One LLM can be used for many different tasks and deployments across organizations, users and applications.
* Performance. Modern LLMs are typically high-performing, with the ability to generate rapid, low-latency responses.
* Accuracy. As the number of parameters and the volume of trained data grow in an LLM, the transformer model is able to deliver increasing levels of accuracy.
* Ease of training. Many LLMs are trained on unlabeled data, which helps to accelerate the training process.
While there are many advantages to using LLMs, there are also several challenges and limitations:
* Development costs. To run, LLMs generally require large quantities of expensive graphics processing unit hardware and massive data sets.
* Operational costs. After the training and development period, the cost of operating an LLM for the host organization can be very high.
* Bias. A risk with any AI trained on unlabeled data is bias, as it's not always clear that known bias has been removed.
* Explainability. The ability to explain how an LLM was able to generate a specific result is not easy or obvious for users.
* Hallucination. AI hallucination occurs when an LLM provides an inaccurate response that is not based on trained data.
* Complexity. With billions of parameters, modern LLMs are exceptionally complicated technologies that can be particularly complex to troubleshoot.
* Glitch tokens. Maliciously designed prompts that cause an LLM to malfunction, known as glitch tokens, are part of an emerging trend since 2022.
There is an evolving set of terms to describe the different types of large language models. Among the common types are the following:
* Zero-shot model. This is a large, generalized model trained on a generic corpus of data that is able to give a fairly accurate result for general use cases, without the need for additional training. GPT-3 is often considered a zero-shot model.
* Fine-tuned or domain-specific models. Additional training on top of a zero-shot model like GPT-3 can lead to a fine-tuned, domain-specific model. One example is OpenAI Codex, a domain-specific LLM for programming based on GPT-3.
* Language representation model. One example of a language representation model is Bidirectional Encoder Representations from Transformers (BERT), which makes use of deep learning and transformers well suited for NLP.
* Multimodal model. Originally LLMs were specifically tuned just for text, but with the multimodal approach it is possible to handle both text and images. An example of this is GPT-4.
The future of LLM is still being written by the humans who are developing the technology, though there could be a future in which the LLMs write themselves, too. The next generation of LLMs will not likely be artificial general intelligence or sentient in any sense of the word, but they will continuously improve and get "smarter."
LLMs will continue to be trained on ever larger sets of data, and that data will increasingly be better filtered for accuracy and potential bias. It's also likely that LLMs of the future will do a better job than the current generation when it comes to providing attribution and better explanations for how a given result was generated.
Enabling more accurate information for domain-specific knowledge is another possible future direction for LLMs. There is also a class of LLMs based on the concept known as knowledge retrieval -- including Google's REALM (Retrieval-Augmented Language Model) -- that will enable training and inference on a very specific corpus of data, much like how a user today can specifically search content on a single site.
What's likely is that the future of LLMs will remain bright as the technology continues to evolve in ways that help improve human productivity.