Large Language Models: What Are They and Why Are They Hard to Train?

Large language models (LLMs) are computerized language models that consist of an artificial neural network with many parameters (tens of millions to trillions), trained on large quantities of unlabeled text containing up to trillions of tokens, using self-supervised learning or semi-supervised learning achieved by parallel computing. 

LLMs have emerged as a powerful tool for natural language processing (NLP) tasks, such as text generation, summarization, translation, question answering, and more. They are general-purpose models, meaning they can handle a wide range of tasks without requiring much task-specific fine-tuning or data. This is because they have learned the syntax and semantics of human language, as well as general “knowledge” about the world, from the vast amount of text they have been exposed to during training. 

However, training LLMs is not an easy task. It requires a lot of computational resources, time, data, and energy. In this blog post, we will explore some of the challenges and trade-offs involved in training LLMs, and some of the possible solutions and alternatives. 

Computational Resources: One of the main challenges of training LLMs is the sheer amount of computational resources needed to process such large models and datasets. For example, GPT-3, one of the most famous LLMs, has 175 billion parameters and was trained on 45 terabytes of text data. To train such a model, one would need access to hundreds or thousands of GPUs or TPUs, which are specialized hardware devices for accelerating deep learning computations. 

However, not everyone has access to such resources, which creates a barrier to entry and innovation for researchers and developers who want to experiment with LLMs. Moreover, the cost of renting or buying such resources can be prohibitive for many individuals and organizations. For example, it was estimated that training GPT-3 from scratch would cost around $12 million . 

One possible solution to this challenge is to use model compression techniques, such as pruning, quantization, distillation, or sparsification, to reduce the size and complexity of LLMs without sacrificing much performance. Another possible solution is to use model-sharing platforms, such as Hugging Face or OpenAI, that allow users to access pre-trained LLMs or fine-tune them on their own data with minimal cost and effort. 

Time: Another challenge of training LLMs is the time it takes to train them. Depending on the model size, data size, hardware configuration, and optimization algorithm, training an LLM can take from days to months. For example, c. This means that training LLMs can be slow and inefficient, especially if one wants to experiment with different model architectures or hyperparameters. 

A solution to this challenge is to use distributed training techniques, such as data parallelism or model parallelism, to split the model and data across multiple devices and speed up the training process. Another possible solution is to use transfer learning techniques, such as pre-training or fine-tuning, to leverage existing LLMs that have been trained on large and diverse datasets and adapt them to new tasks or domains with fewer data and time. 

Data: Another challenge of training LLMs is the data they require. To achieve good performance on various NLP tasks, LLMs need to be trained on large and diverse datasets that cover a wide range of topics, domains, languages, and styles. However, collecting and curating such datasets can be difficult and expensive. Moreover, not all data is equally useful or relevant for LLMs. Some data may be noisy, redundant, outdated, biased, or harmful. 

This can be solved by using data filtering techniques, such as deduplication, quality scoring, diversity sampling, or content moderation, to select the most relevant and useful data for LLMs. Another possible solution is to use data augmentation techniques, such as back translation, paraphrasing, or perturbation, to generate more synthetic data from existing data and increase the diversity and robustness of LLMs. 

Energy: Another challenge of training LLMs is the energy they consume. Training large neural networks on large datasets requires a lot of electricity, which contributes to greenhouse gas emissions and climate change. For example, it was estimated that training GPT-3 emitted about 1, 400 tons of carbon dioxide, which is equivalent to the lifetime emissions of five average American cars. This means that training LLMs can have a negative impact on the environment and society. 

A potential way to overcome this challenge is to use energy-efficient techniques, such as low-precision arithmetic, sparse computation, or reversible computation, to reduce the energy consumption and carbon footprint of LLMs . Another possible solution is to use energy-aware techniques, such as adaptive learning rate, early stopping, or checkpointing, to optimize the energy-performance trade-off of LLMs. 

In this blog post, we have discussed some of the challenges and trade-offs involved in training large language models, and some of the possible solutions and alternatives. We have seen that training LLMs requires a lot of computational resources, time, data, and energy, which can pose technical, economic, ethical, and environmental issues. However, there are ways to mitigate these challenges.

Let’s talk if your company is considering creating LLM to generate content.