These days information is everywhere! But it's often scattered in unstructured forms, such as what you see in social media, or thousands (or millions) of PDF and Word files in your organisation's storage folders. Extracting valuable insights and ‘fast information’ from unstructured data, such as text documents, web content, or social media posts, is a significant challenge. However, with the advent of Large Language Models (LLM’s) like LlaMa, we now have a powerful tool to convert this unstructured data into structured gold.

Understanding the Challenge

Converting unstructured data into a structured format is essential for various applications, including data analysis, information retrieval, and knowledge management. Unstructured data lacks a clear organisation and formatting which is found in structured databases. Manual conversion to structured data is extremely time consuming and has high risks of missing data and human error. It's often a mishmash of words, sentences, and paragraphs, in a wide variety of formats which makes it difficult for machines to grasp its meaning and to structure it.

The Conversion Process

In IM Systems, we have developed a solution to convert unstructured data into structured information using a step-by-step process generating concise data sets out of unstructured data. We begin with identifying the data sources and utilise Transformer neural networks to encode unstructured data into vectorised information. Then by embedding these vectors into a private large language model, we can analyse and comprehend the unstructured data. We then provide a decoding instruction to the LLM model to provide a form of structured data which is easily reformable into any standard structure. This structured data is then systematically validated for accuracy, populated and integrated into various systems and databases while maintaining continuous updates to reflect changes in the source data. This can be a fully automated process, or may include manual verification checkpoints.

Our developed solution architecture is capable of isolating multiple project spaces using the same LLM core. Together with using private LLMs, we are able to create a highly secure environment (private cloud / on-premise) to manage the conversion for confidential data, even as a totally offline environment.

The Power of Language Models

Large language models demonstrate significant performance at understanding human generated text. They can analyse vast amounts of unstructured data quickly and accurately and generate structured data. By utilising appropriate embedding solutions, these models can be tailored to specific conversion tasks, making them even more effective. They also create a knowledge base of the unstructured data and can answer questions related to the documentation and provide exact references to the original documents.

Real-World Applications

Converting unstructured data into structured data has wide-ranging applications. It can be used to organise research papers, extract customer feedback from reviews, categorise news articles, and much more for both public and private sectors. Using our LLM’s solution, structured data can be provided in real-time, making it possible to support immediate data driven decision making.

Challenges and Considerations

While language models are powerful, challenges remain. Quality of source data, model accuracy, and domain-specific knowledge all play a role in the success of the conversion process. It's essential to continuously monitor and update structured data to reflect changes in the unstructured source.

Conclusion

The ability to convert unstructured data into structured information in real-time is a game-changer in the data-driven era. Large language models offer a promising solution to this challenge, enabling governments, organisations and researchers to unlock valuable insights from the vast sea of unstructured data. Our developed solution at IM Systems is at the forefront of this transformative field. As we continue to advance in the field of natural language processing, we can expect even more sophisticated tools for this critical task, driving innovation across various industries.