Training Large Language Models: Cracking the Language Code

Training Large Language Models
Table of Contents

With the rapid progression in computational capabilities and the capacity to manage extensive datasets, artificial intelligence (AI) has seamlessly blended into various aspects of daily life. 

Its presence is evident in connected home devices, smartphones, advanced driving assistance systems (including autonomous vehicles), chatbots, and even real estate platforms.

Large language models (LLMs) significantly contribute to the advancement of AI applications. These models augment and refine AI functionalities and are now widely available to the public through platforms like OpenAI’s ChatGPT and other generative AI tools.

Keep reading to discover further insights into how large language models are trained and much more.

What are Large Language Models?

A large language model (LLM) is an AI architecture trained on vast datasets from various online sources, encompassing literature, digital media, and different textual materials. 

It uses deep learning to understand and perform natural language processing (NLP) tasks, such as summarization and generation, drawing insights from their input and training data.

These models work like the human brain’s neural networks and can be trained for various applications beyond language processing, such as analyzing protein structures or writing software code. During training, they accumulate extensive knowledge from the data they’re exposed to.

LLMs are trained on datasets exceeding one petabyte, with each gigabyte containing roughly 180 million words. This extensive data intake enables them to comprehend and generate diverse content formats, ranging from text to audio, images, and synthetic data. 

Typically, these models are usually pre-trained and then fine-tuned for specific tasks to adapt to different use cases.

Having explored the foundational concepts of LLMs and their broad capabilities, it’s important to look into the process of training these models to harness their full potential.

Video source: YouTube/simpleshow

Exploring LLM Training

LLM training entails exposing these models to huge amounts of textual or multi-modal data, a foundational step enabling them to recognize patterns and anticipate linguistic elements. As a result of this training, LLMs become adept at generating human-like text, translating languages, and even responding to inquiries.

Understanding Model Size and Parameters

Central to comprehending LLMs is understanding the significance of their size, often denoted by the number of parameters they possess. These parameters act as the variables guiding the model’s predictions. 

Larger models, with more parameters, boast a more nuanced understanding of language, though at the cost of increased computational demands. Thus, while larger models offer enhanced proficiency, they require substantial computational resources and specialized expertise.

Data Requirements for Training

A critical consideration in LLM training is the volume of the data needed to achieve optimal performance. This requirement varies depending on factors such as the complexity of the model’s architecture and the desired level of proficiency. 

In general, LLMs like GPT-4, also used in various conversational applications, undergo training on datasets of different sizes, typically ranging from gigabytes to terabytes. However, the crucial aspect lies in ensuring the dataset’s diversity and representativeness, encompassing a wide array of language patterns and nuances.

Optimizing Model Performance

Achieving optimal model performance hinges on the quality of the dataset used for training. A well-curated dataset comprising diverse examples relevant to the model’s intended application is most important. Such datasets often yield superior performance compared to larger but less relevant datasets.

How are Large Language Models Trained

The training process entails utilizing a high-quality, extensive dataset. Throughout this phase, the model continually refines its parameter values to accurately predict the next token based on the preceding sequence of input tokens, employing self-learning techniques to optimize parameters to maximize the probability of upcoming tokens in the training instances.

Here’s an overview of the process involved in training large language models:

1. Data Gathering (Preprocessing)

The training begins by harnessing a vast, high-quality dataset gathered from various sources, such as books, articles, web content, and publicly available datasets. 

During this phase, the model iteratively adjusts its parameter values, aiming to accurately predict the subsequent token based on the preceding sequence of input tokens. This is facilitated by employing self-learning techniques that guide the model in optimizing parameters to maximize the likelihood of upcoming tokens in the training instances. 

This dataset is then subjected to cleaning and preprocessing steps, such as lowercase conversion, stop word elimination, and tokenization into sequences.

2. Model Configuration

The backbone of this process lies in the use of special programs made for understanding NLP tasks called “transformer-based deep learning frameworks.” Setting up these programs involves deciding how many layers they have, how they pay attention to different parts of the text, and other important settings.

These programs use building blocks called transformer blocks to process information and determine what’s important. They also have parts called attention heads to focus on different parts of sentences and settings called hyperparameters that control how they learn. 

Researchers make these programs work better by trying different settings to see which ones work best for the job.

Video source: YouTube/StatQuest with Josh Starmer

3. Model Training

Following data preparation, the cleansed text data is used to train the model. This training commences with the model processing a sequence of words and predicting the subsequent word in the sequence. 

Subsequent word predictions guide the model in iteratively adjusting its weight assignments. This iterative process repeats millions or even billions of times, contingent upon the dataset’s scale, until optimal performance is attained.

Given the extensive scale of LLM models and data, substantial computational resources are essential for training. To expedite training, leveraging model parallelism is common, which entails distributing segments of the model across multiple graphics processing units (GPUs).

4. Fine-Tuning

Upon completion of training, the model undergoes evaluation using a separate testing dataset to assess its performance. Depending on the outcomes of these evaluations, fine-tuning adjustments may be implemented. 

These refinements encompass tweaking hyperparameters or altering the model’s architecture. In certain scenarios, additional data training may be required to augment the model’s efficacy.

LLMs’ Post-Training Evaluation Methods

After training, LLMs undergo evaluation to assess their success and compare them to benchmarks or previous versions. Evaluation involves intrinsic and extrinsic methods.

  • Intrinsic methods focus on objective metrics like language fluency, coherence, perplexity, and BLEU (Bilingual Evaluation Understudy) score. Language fluency checks LLM-generated text’s grammatical correctness and naturalness, while coherence ensures logical connections between sentences. Perplexity measures the model’s prediction accuracy, while the BLEU score evaluates translation quality.
  • Extrinsic methods assess LLM performance in real-world tasks such as problem-solving and reasoning. These methods include questionnaires comparing LLM performance to humans, testing common-sense inferences, multitasking across domains, and evaluating response factuality. Recent advancements favor extrinsic methods due to their relevance to practical applications.

Challenges in Training Large Language Models 

Training large language models from scratch poses significant challenges. Here are some of the primary challenges involved in training LLMs from scratch:

  • Infrastructure: LLMs require massive text data, often exceeding 1000 GB, and using models with billions of parameters, requiring infrastructure equipped with multiple GPUs for efficient training. For instance, training GPT-3, which has 175 billion parameters, on a single NVIDIA V100 GPU would take 288 years, highlighting the need for parallel processing on thousands of GPUs, as seen with Google’s Pathways Language Model (PaLM) model trained on 6,144 TPU v4 chips.
  • Cost: While distributed training on numerous GPUs is essential, it presents a significant financial burden, making it unfeasible for many organizations. Even OpenAI, the developer of GPT models, relied on Microsoft Azure cloud resources due to cost constraints, with Microsoft investing $1 billion in OpenAI in 2019 to support LLM training.
  • Model distribution strategies: Effective LLM training begins with single GPU training to evaluate resource requirements, followed by employing model parallelism and Tensor model parallelism. These techniques distribute individual layers of the model across multiple GPUs to enhance memory and input/output bandwidth usage, ensuring efficient data transfer between storage devices and processing units. This process demands meticulous coding and configuration adjustments to meet the model’s needs and accommodate available hardware resources.
  • Impact of model architecture choices: Selecting an appropriate LLM architecture significantly influences training complexity. Factors like model depth, width, and the inclusion of residual connections must be carefully considered to strike a balance between computational resources and complexity. 

Moreover, understanding the specific functional requirements of the model, such as generative modeling, multi-modal analysis, or multi-task learning, and experimenting with well-established architectures like GPT, BERT (Bidirectional Encoder Representations from Transformers), and XLNet (pre-training method for NLP built), alongside tokenization techniques, further shapes training strategies to suit individual use cases.

In a recent development, Amazon trained the largest text-to-speech model to date BASE TTS, a 980M parameter LLM, on 100,000 hours of audio, shedding light on the intricate infrastructure and computational resources essential for such endeavors. This initiative underscores the substantial investment, particularly in GPUs, necessary to effectively train LLMs.

Video source: YouTube/CuriosityKilledTheBordom

Conversely, the breakthrough achieved by DeepMind and the University of Southern California’s “self-discover” prompting framework marks a significant leap in LLM reasoning capabilities, offering invaluable insights into overcoming challenges related to model architecture and computational complexity. Their breakthroughs provide essential guidance for addressing key hurdles in LLM training, including infrastructure requirements and model distribution strategies.

Training Large Language Models: Key Takeaways

The evolution of Large Language Models (LLMs) marks a profound integration of artificial intelligence (AI) into diverse facets of daily life, from connected home devices to advanced driving assistance systems. LLMs, such as OpenAI’s ChatGPT, significantly enhance AI capabilities, offering refined natural language processing functionalities to the public.

Exploring LLM training reveals the complex process of exposing models to vast textual data to refine their understanding and generation of human-like text. This training requires extensive datasets and meticulous optimization to achieve optimal performance.

However, training LLMs from scratch presents notable challenges, including:

  • Infrastructure,
  • Cost,
  • Model distribution strategies,
  • Impact of model architecture choices. 

These challenges highlight the complexities of training LLMs and emphasize the need for innovative solutions to overcome them. 

As LLM technology advances, effectively tackling these challenges will be essential for unlocking the maximum potential of these models and driving progress in AI applications across diverse domains. Therefore, ongoing efforts to address these obstacles will serve to mold the future of AI and maximize the capabilities of large language models.

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.