Labelling Options in LLMs

When you're dealing with many large datasets for deep transformer neural networks, labelling is an essential step in organizing and understanding your data. Labelling helps you categorise and annotate reports based on their content or characteristics. It is so important that we felt we should detail the broad options which are available. Labelling is pretty much the wheels on the AI racing car. Here are some options for labelling large datasets of reports for deep transformer neural networks …

Binary Classification Labels

Positive and Negative Labelling. You can label reports as either positive (relevant or containing specific information) or negative (irrelevant or lacking relevant information). This approach is common for tasks like sentiment analysis.

Multiclass Classification Labels

Categorization. Label reports with specific categories or topics that they belong to. For instance, if you're dealing with risk reports for mines, labels could be "UG Hard Rock," "Tailings Storage Facility" "OC Coal" etc.

Hierarchical Labels

If your reports can belong to multiple categories or have a hierarchical structure, you can use hierarchical labels. For instance, you might have labels like "Technology > Artificial Intelligence > Deep Learning" to indicate a hierarchical relationship.

Numeric Labels

You can assign numerical labels to reports to represent a continuous variable. For example, if you're dealing with financial reports, you might label them with numerical values representing profit or loss percentages, or risk values, or even general monetary values.

Custom Labels

Depending on the specific task, you might need custom labels that are relevant to your application. These labels should be defined based on the problem you are trying to solve. Normally there would? be the possibility of multiple labels, to account for different interests.

Entity Recognition

If you need to extract specific entities or information from reports, you can use entity recognition labels. For instance, in mine risk reports, you might label risks, loss scenarios, and recommendations.

Event Extraction Labels

If your reports describe events, you can label them with information like event type, date, location, and participants.

Sentiment Labels

If you're analysing sentiment, you can use labels such as "Positive," "Neutral," and "Negative" to categorize the sentiment expressed in the reports.

Keyword/Tag Labels

Assign labels based on keywords or tags that are relevant to the content of the reports. This can help with search and retrieval tasks.

Anomaly Detection Labels

If you are looking for anomalies or outliers in your reports, label them as either "Normal" or "Anomalous."

Temporal Labels

For time-series data, you might label reports with timestamps or time intervals to analyse trends and patterns over time.

Geospatial Labels

If your reports have geospatial information, you can label them with geographic coordinates or regions.

User-Defined Labels

Allow users or domain experts to define custom labels that make sense for your specific application.

It is vital to select labelling strategies that align with your specific LLM task and the goals of your project.

Additionally, consider using annotation tools, automation, and guidelines to ensure consistency and accuracy in your labelling process, especially when dealing with a large dataset.