A large language model (LLM), no matter its inherent architectural sophistication, is only as brilliant as the information it is fed. Businesses looking to take advantage of the potential of LLMs need to understand the crucial role of data quality.
An LLM learns from the data it is given. This repository (or ‘corpus’) encompasses everything from libraries to academic articles, web content, proprietary documentation, customer reviews and social media posts.
The LLM builds a sophisticated internal map based on what it learns from. This enables it to generate human-like text, answer questions, summarise information and even translate languages.
The sheer scale of data involved in pre-training LLMs is mind-boggling, which is why data management and quality are critical. If flawed or compromised data is introduced, the outputs will reflect this.
Poor data quality can lead to biased and inaccurate outputs (commonly called ‘hallucinations’), and decreases in performance, reliability and trustworthiness. It can also increase operational costs when it has to be retrained, and compliance and legal risks if sensitive, proprietary, or inherently biased data is used.
Five steps to trustworthy data for LLMs
Data sourcing and collection: Precision over volume
When acquiring data, ensure it is reputable, relevant and includes sufficiently diverse sources that align with the LLM’s objectives and intended use.
Proprietary data that reflects the nuances of customers, operations and markets is key as it enables models to deliver relevant, accurate and useful outputs.
For any South African business eyeing the potential of LLMs, investing heavily in data quality isn't optional.
If the LLM is designed to enhance customer service interactions, the collection strategy should prioritise customer feedback, frequently asked questions (FAQs) and detailed product manuals, for example.
Data cleaning and pre-processing: The art of refinement
Raw data is almost never pristine. It needs to be cleaned to identify and eliminate errors, inconsistencies, duplicate entries and irrelevant information. This involves correcting typos, standardising data formats, handling missing values, and filtering out spam, malicious code, or offensive language.
Data labelling and annotation: Adding intelligent layers
For many LLM applications, raw text alone is insufficient. Data labelling and annotation involve categorising or tagging elements in the dataset to provide context and structured information.
While labour-intensive, labelling and annotating significantly improve the model's ability to understand nuances, extract specific information and accurately perform complex tasks.
Data validation and quality assurance: Rigorous verification
Before data is introduced to the LLM for training, it must be validated. This involves cross-referencing against benchmarks, established ground truths and verified sources to confirm its accuracy, internal consistency and statistical representativeness. This helps to identify and rectify residual errors, inherent biases, or unforeseen gaps before they spread.
Data governance and maintenance: A continuous imperative
Ensuring data quality for LLMs is ongoing as long as the LLM is used. Establish clear policies and processes to collect and store data, control access to the LLM and make sure it is used ethically. Regularly update and refresh datasets to ensure the LLM remains current, relevant and responsive to evolving real-world dynamics.
In the retail sector, this is what this process could look like:
Steps one and two: Customer feedback collection
Collect verified customer feedback from the company’s e-commerce platform, in-store surveys and reputable product review sites. Clean the data to correct conversational slang, abbreviations, spelling mistakes and so on. Remove duplicates or irrelevant content to make sure the dataset is clean and directly relevant to the objective.
Step three: Product information labelling
Label key features (eg, ‘material: 100% cotton’, ‘fit: slim’, ‘warranty: two years’), unique selling propositions (eg, ‘eco-friendly’, ‘proudly South African’), and even common customer questions. This information allows the LLM to provide accurate and specific answers to queries like: ‘Does this shirt shrink?’ or ‘What are the care instructions for this fabric?’
Step four: Performance validation
Test rigorously. Test the LLM with thousands of typical customer questions, compare its answers against known correct responses, trace errors or discrepancies back to the data and remedy appropriately.
Step five: Continuous improvement
As things change, add new information and refine data sets. This ensures the LLM remains current, preventing it from recommending outdated stock or providing irrelevant advice based on old information.
For any South African business eyeing the potential of LLMs, investing heavily in data quality isn't optional. Without curated and trustworthy data, you're training a sophisticated parrot that's learned from a garbled conversation. In today's fiercely competitive retail landscape where customer experience is king, that's a risk no one can afford to take.
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