- Large language models (LLMs) are AI models that specialize in natural language processing and natural language generation.
- LLMs have millions or billions of parameters, the factors the model weighs when calculating an output.
- LLMs are trained in two phases: pre-training on large amounts of unlabeled data, then fine-tuning on a smaller labeled data set.
- An LLM's knowledge is limited to the data available when it was trained, and it reflects any bias in that data.
- LLM output can sound convincing even when it contains false information.
What are Large Language Models?
Large language models (LLMs) are sophisticated artificial intelligence (AI) models that specialize in natural language processing (NLP) and natural language generation tasks. LLMs utilize deep learning, fine-tuning, and in-context learning in order to achieve high-quality results. LLMs are trained on extremely large datasets in order to learn the complexities of language and generate natural responses to text input. The complexity of a language model is measured by the number of parameters it uses, with parameters being the factors considered when calculating outputs. For comparison, LLMs have millions or billions of parameters.
How are Large Language Models Trained?
Large language models are trained in two phases, pre-training and fine-tuning. The pre-training phase uses large amounts of unlabeled data in order to build out the basic parameters for analyzing language. This is done in a series of steps:
- Text data is processed into a numerical representation that the model can interpret.
- The model’s parameters are set randomly.
- The prepared data is fed to the model.
- Using a loss function, the developers calculate the difference between the model’s output and the intended output.
- The model’s parameters are adjusted to reduce loss.
- The tuning process is repeated until the desired accuracy is achieved.
The pre-training phase requires a significant amount of time and computing power due to the size of the data set and the complexity of the parameters.
The fine-tuning phase is intended to prepare the model for its specific end purpose. In order to fine-tune the model, the tuning process is repeated but with a smaller, labeled data set, that is relevant to the intended purpose of the model. Comparatively, fine-tuning requires significantly less data and computing power than pre-training.
Benefits of Large Language Models
Automation of Tasks
LLMs can be trained to automate a variety of business tasks in order to reduce labor costs and save users time. Processes that can be automated include sentiment analysis, basic customer service, content creation, and fraud detection.
Enhanced Customer Satisfaction
LLMs can be trained to communicate with customers to aid in troubleshooting and other simple customer service tasks. An LLM allows businesses the availability to reply to customer requests 24/7, boosting customer satisfaction.
Human Error Reduction
Automating tasks with an LLM can reduce human error and increase the accuracy of business processes. Tasks such as sentiment analysis can be objectively judged, having been trained on thousands of data points.
Challenges of Large Language Models
Outdated Data
An LLM’s knowledge and capabilities are limited by the data available at the time it is trained. It will not understand new developments in technology, science, or business without additional training. Updating the model requires retraining with additional data, which is a complicated and expensive process. Even with new data, it can be very difficult to override outdated or incorrect knowledge once a model has been trained.
Bias and Factual Inaccuracy
Similarly, a language model will reflect any bias, false information, or negative language found in the data it was trained on. Data has to be carefully prepared and examined in order to avoid these issues, but cases still occur of LLMs giving false information or generating inappropriate language. Because of the way LLMs work, generated text can appear very convincing even when it contains false information.
Environmental Concerns
The process of developing and training LLMs requires intense computing power. Oftentimes, models run on systems containing hundreds of high-end processors and GPUs which require power to run as well as cooling and other maintenance. This energy use leaves behind a large carbon footprint, contributing to pollution and climate change.
The Future of Large Language Models
LLMs are still in the early stages of their development and will continue to advance rapidly in the near future. Some of the advances on the horizon may include:
- Self Improvement - When humans perform a task or generate content, they are able to receive feedback which they can use to improve future results. AI models may be capable of learning similarly in the future. Research is being done to develop language models that are able to use their own generated text in order to further hone their parameters and improve their output.
- Built-In Fact Checking - LLMs are very capable of assembling information into natural and convincing output. However, they are not able to verify the accuracy of the message they are generating. In the future, LLMs may gain the capability to utilize outside sources in order to fact-check their work and provide citations to back up their claims.
- Parameter Optimization - LLMs can contain billions of parameters created to analyze speech of all types. Dense models are designed so that all of their parameters activate in order to generate a response. This allows LLMs to respond to almost anything, but it makes them very inefficient as most parameters will not be relevant to the task at hand. Mixture-of-experts LLMs already activate only a relevant subset of their parameters for each input, improving compute efficiency and response speed.
Large Language Models in H2O AI
H2O Cascade (next-gen H2O Enterprise LLM Studio) fine-tunes LLMs on your own data, and Enterprise h2oGPTe uses LLMs to answer questions over your documents. H2O also publishes open small language models, the H2O Danube series.