1. Data concerns and considerations
So far, we have discovered how large language models (LLMs) are changing the AI landscape, especially in how we use language, and summarized how they are constructed.
2. Data considerations
In this video, we will examine the data considerations to build these large models, such as data volume and compute power, quality of data, labeling status, bias in data, and data privacy.
3. Data volume and compute power
We will discuss them one by one, starting with data volume and compute power.
Think about how a child learns to talk. They need to hear lots of words, many times over, to start talking. Training LLMs is similar - they need a ton of data to learn language patterns and structures. Recall that an LLM may need 570 GB of data to train, which is equivalent to 1-point-3 million books.
4. Data volume and compute power
The computing power needed to process this sheer magnitude of data is extensive. Think of the extent of energy consumption that goes into its making, something we will discuss in the next video.
To give an estimate of scale, training one such model can cost millions of dollars worth of computational resources.
5. Data quality
The next factor is high-quality data, which is crucial to train an LLM. Accurate data leads to better learning and improved generated response quality, building trust in its outputs.
Let's go back to the child learning to talk. They will learn what they have heard, even if it's gibberish.
The same goes for LLMs. They will produce low-quality outputs if we train them with data full of mistakes or poor grammar.
6. Labeled data
Ensuring correct data labeling is crucial for training LLMs as it enables the model to learn from accurate examples, generalize patterns, and generate accurate responses.
However, this process can be labor-intensive due to the large amount of data. For example, when training an LLM to categorize news articles like 'Sports', 'Politics', or 'Technology', assigning the correct label requires significant human effort.
Misclassifications, or errors, occur when articles are assigned incorrect labels, impacting the model's reliability and performance. To address these errors, the labels are identified and analyzed, leading to iterative model refinement.
7. Data bias
Ensuring bias free data is as important as its quality and accuracy for any model including LLMs.
Bias occurs when the model's responses reflect societal stereotypes or lack diverse training data, leading to discrimination and unfair outcomes.
For example, a sentence starting with "The nurse said that..." might be more likely to be completed with a female pronoun like "she".
To address biases, we must actively evaluate the training data for imbalances, promote diversity, and employ bias mitigation techniques, which can include augmenting the dataset with more diverse examples.
8. Data privacy
Even if the data has good-quality labels, we also need to consider compliance with data protection and privacy regulations.
The data may contain sensitive or personally identifiable information (PII).
Privacy is a big deal when it comes to data.
Training a model on private data without permission, even if the identifying details are anonymized, can breach privacy, leading to legal consequences, financial penalties, and reputational harm.
The relevant permissions need to be obtained so that data privacy laws are followed.
9. Let's practice!
Now that we have covered the crucial aspects of data considerations and privacy concerns related to LLMs, it's time to put your understanding to the test.