LLMs Help AI Generate Human-Like Text. Here’s How These Models Are Trained

Training LLMs are resource-intensive, involving advanced machine learning techniques, vast data, and powerful computing resources.

At the core of any LLM is a deep neural network, which functions similarly to the human brain, recognising patterns in data.

Source : Mohamed Hassan/Pixabay

By Ankush Sabharwal

Large language models (LLMs) are making headlines in the realm of artificial intelligence (AI) l for their ability to generate human-like text. However, few understand the extensive and complex process that goes into training these powerful models. This article unpacks the key steps and best practices involved in training LLMs, shedding light on the immense computational effort behind their development.

Understanding LLM Architecture

At the core of any LLM is a deep neural network, which functions similarly to the human brain, recognising patterns in data. The transformer allows models to process input text in parallel, making it more efficient than older architectures like RNNs (Recurrent Neural Networks) or LSTMs (Long Short-Term Memory networks).

Transformers rely heavily on two mechanisms:

Self-Attention: Helps the model focus on different parts of the input sequence at different stages.

Positional Encoding: Adds information about the order of words, which helps in understanding context.

Steps In Training An LLM

1. Data Collection

Training begins with a massive amount of data. LLMs are typically trained on hundreds of gigabytes to several terabytes of text data from a variety of sources, including books, websites, academic articles, and social media. 

The choice of data is critical to ensure the model learns a diverse and balanced representation of language. However, data preprocessing is equally important. This includes removing errors, duplicates, and irrelevant content, and tokenising the text into a format understandable by the model.

2. Tokenisation

LLMs do not work with raw text but instead break down input into smaller units called tokens. These tokens could be words, subwords, or characters, depending on the tokenisation strategy. For example, the word "computer" might be tokenised into "com" and "puter" in a subword approach.

The size of the vocabulary, or the total number of tokens the model recognizes, can significantly impact the model's performance. 

3. Model Training

After tokenisation, training starts by feeding tokens into the transformer using unsupervised learning. The model predicts the next token in a sequence, adjusting weights to enhance accuracy and generate coherent text. Training LLMs can take weeks to months, often needing specialised hardware like GPUs or TPUs.

To improve the efficiency of model training, Quantisation techniques are often applied. Quantisation converts large sets of input values into smaller, more manageable sets, reducing computational demands without sacrificing accuracy. Popular methods include:

• GPTQ: A quantisation tool often used for Transformer models, optimised for faster GPU performance.
• GGML: A quantisation method suited for CPUs, slightly larger in size but offering improved CPU efficiency.

4. Fine-Tuning

Once the model is pre-trained, it can be fine-tuned on specific tasks like translation, summarisation, or sentiment analysis. Fine-tuning involves additional training on a smaller, task-specific dataset. Fine-tuning not only improves the model’s performance but also tailors it to the nuances of specific use cases.

Parameter-Efficient Fine-Tuning vs. Fine-Tuning

Parameter-efficient fine-tuning (PEFT) updates fewer model parameters, offering faster, more cost-effective results than full fine-tuning, which is more resource-intensive and prone to overfitting.

Synergising diverse data improves accuracy but risks contamination, privacy issues, and biases, raising ethical concerns in LLM development.

5. Evaluation and Testing

Finally, LLMs are tested rigorously to ensure they produce high-quality, accurate results. This involves:

• Perplexity scores, which measure how well the model predicts the next token.
• Human evaluation to assess coherence, relevance, and fluency.

Best Practices For LLM Training

1. Data Diversity: Ensuring a broad dataset helps in building a model that generalises well across languages, topics, and styles. More diverse data leads to more robust models.
2.  Efficient Compute Use: Leveraging cloud computing platforms and specialised hardware can significantly reduce training time.
3. Ethical Considerations: Data curation should ensure that sensitive information is excluded and that the model does not perpetuate harmful biases. For instance, many researchers are working on debiasing techniques to mitigate gender and racial stereotypes that LLMs might inadvertently learn from their training data.

Training LLMs are resource-intensive, involving advanced machine learning techniques, vast data, and powerful computing resources. The future lies in enhancing efficiency, fairness, and adaptability for real-world use. As AI evolves, understanding LLM training mechanics is essential for researchers, developers, and policymakers to drive innovation responsibly.

(The author is the Founder and CEO of CoRover)

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