With their ability to generate anything and everything required (from job descriptions to code), large language models have become the new driving force of modern enterprises. They support innovation across functions, allow teams to be more productive and offer insights that can scale businesses to new heights.

According to McKinsey, the potential of LLMs like GPT-4 is such that they can increase annual global corporate profits by up to $4.4 trillion. Goldman Sachs also predicts that the generative technology can add almost $7 trillion to the global economy and lift productivity growth by 1.5 percentage points in the next decade.

But, here’s the thing. Like all things AI, language models also need clean, high-quality data to do their best. These sophisticated systems work by picking up on patterns and comprehending subtleties from training data. If this data is not up to the mark or contains too many gaps/errors, the model’s capacity to produce coherent, accurate and relevant information naturally declines.

Here are some strategic tactics that can put data affairs in order while adhering to the highest preparation standards and make organizations ready for the age of generative AI.

Define Data Requirements

The first step in building a well-functioning large language model is data ingestion. It involves collecting massive unlabeled datasets for training the model. However, instead of diving right away and scraping everything possible to train the LLM, it is suggested to define the requirements of the project, like what kind of content (general-purpose content, specific content, code, etc.) it is expected to generate.

Once a developer has considered the targeted function, they can choose the type of data needed and pick the sources for scraping it. Most general-purpose models, including the GPT series, are trained on data from the web, covering sources like Wikipedia and news posts. This can pulled up using libraries like Trafilatura or specialized tools. Not to mention, there are also many open source data libraries for use, including the C4 dataset, used for Google’s T5 models and Meta’s Llama models and The Pile from Eleuther AI

Clean And Prepare The Data

After gathering the data, teams have to move towards cleaning and preparing it for the training pipeline. This requires multiple layers of handling at the dataset level, starting with the identification and removal of duplicates, outliers and irrelevant/broken data points that do not help build the language model or may affect its output accuracy in any way. Further, developers have to take into account aspects like noise and bias. For the latter, in particular, oversampling the minority class could be an effective way to balance the distribution of the classes.

If certain information is needed for the model’s decisioning but is missing out on some data points, statistical imputation techniques can be used to fill in the blanks with substitute values. Tools such as PyTorch, Sci Learn and Data Flow can come in handy when preparing a high-quality dataset.

Normalize It

Once the data is cleansed and de-duplicated, it has to be transformed into a uniform format through data normalization. This step reduces the dimensionality of the text and facilitates easy comparison and analysis – allowing the model to treat each data point the same way.

For comparing the usefulness of the information, values measured on different scales are translated to a standard theoretical scale (1 to 5). In the case of text data, changes frequently made are conversion to lowercase, removal of punctuations and conversion of numbers to words. This can easily be achieved with the help of text processing packages and NLP.

Handle Categorical Data

Sometimes, scraped datasets can also include categorical data, grouping information with similar characteristics (race, age groups or education levels). This kind of data should be converted into numerical values in order to be prepped for language model training.

To do this, three coding strategies can normally used: Label encoding, One-hot encoding and Custom binary encoding.

Label encoding assigns unique numbers to distinct categories and is suited for nominal data. One-hot encoding creates new columns for each category, expanding dimensions and enhancing interpretability. And, finally, custom binary encoding strikes a balance between the first two to mitigate dimensionality challenges. One should experiment with each of these two to see which works best for the data at hand.

Remove Personally Identifiable Information

While extensive data cleaning, as detailed above, helps ensure model accuracy, it does not guarantee that any personally identifiable information (PII) included in the dataset will not appear in the generated results. This could not only be a major breach of privacy but also draw unwanted attention from regulators.

To prevent this from happening, try removing or masking PII such as names, social security numbers and health information using tools like Presidio and Pii-Codex. This step should be performed before using the model for pre-training.

Focus on Tokenization

A large language model processes/generates clear, concise output using basic units of text or code called Tokens. In order to create these tokens for the system, one has to split the input data into distinct words or phrases (smaller units). It is suggested to go for word, character or sub-word tokenization levels to adequately capture linguistic structures and get the best results.

Don’t Forget Feature Engineering

Since the performance of the model directly depends on how easily the data can be interpreted and learned from, it remains essential to look at the aspect of feature engineering. As part of this, one has to create new features from raw data, extracting relevant information and representing it in a way that makes it easier for the model to make accurate predictions.

For example, if there’s a dataset of dates, one might create new features like day of the week, month or year to capture temporal patterns.

Today, feature engineering is a fundamental step in LLM development and critical to bridging any gaps between text data and the model itself. In order to extract features, try leveraging techniques like word embedding and utilizing neural networks for representation. Key steps here include data partitioning, diversification and encoding into tokens or vectors.

Accessibility is Key

Having the data in hand but not giving the model full access to the pipeline could be a big blunder in LLM development. This is why, as and when the data is preprocessed and engineered, it should be stored in a format accessible to the large language models in training.

To do this, one could choose between file systems or databases for data storage and maintaining structured or unstructured formats.

At the end of the day, data handling at all levels – from acquisition to engineering – remains critical for AI and LLM projects. Teams can start their journey to successful model training, and ensuing growth, by preparing a checklist of steps, which could ultimately reveal insights and opportunities for improvement. The same checklist could also be used to improve existing LLM models.

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