top of page
Writer's pictureEditorial Staff

Data Collection


Importance of High-Quality Data for SLMs

SLMs are designed to perform well on specific tasks with limited computational resources. Unlike large language models (LLMs) that are trained on massive amounts of diverse data from the internet, SLMs require carefully curated, high-quality datasets tailored to their target domain and use case.

The quality and relevance of the training data is crucial for SLMs to achieve good performance, as they have a smaller parameter count and cannot rely on brute force to learn from noisy data like LLMs. Techniques like data augmentation and transfer learning can help, but the foundation is having the right data.


Key Considerations for SLM Data Collection

When collecting data for an SLM, keep the following in mind:

  • Relevance: The data should be highly relevant to the specific task and domain the SLM will be applied to. Irrelevant data can introduce noise and reduce performance.

  • Quality: Prioritize data from authoritative, reputable sources. Noisy, low-quality data can lead to suboptimal model performance.

  • Diversity: While the data should be focused, it's important to include a diversity of examples to cover edge cases and improve generalization.

  • Privacy and Ethics: Ensure the data collection process adheres to privacy regulations and ethical guidelines. Anonymize sensitive information.

  • Licensing: Verify that the data can be used for training machine learning models and that you have the necessary licenses and permissions.


Data Collection Techniques for SLMs

Some common techniques for collecting data for SLMs include:


  • Web scraping: Carefully scrape relevant web pages and documents, ensuring compliance with robots.txt and terms of service.

  • Crowdsourcing: Use platforms like Amazon Mechanical Turk to collect human-generated data like annotations, translations, or conversational dialogues.

  • Internal data: Leverage proprietary data from the organization's internal systems, such as customer support logs, product documentation, or industry reports.

  • Synthetic data generation: Use techniques like template-based generation or machine learning models to synthetically generate additional training examples.

Comments


Top Stories

Check back soon
Once posts are published, you’ll see them here.
bottom of page