Unstructured data accounts for nearly 90% of data generated by organizations. [1] Most of this data comes in the form of documents like reports, emails, social media posts, and much more. Unfortunately, without a proper analytics system, businesses have a limited ability to drive meaningful Insights from these mountains of data.

The inception of large language models like OpenAI’s ChatGPT has provided a vast potential for automatically extracting insights from unstructured data, prompting many organizations to jump on the LLM bandwagon. But even the most advanced LLMs come with a few limitations, including model hallucination and bias, thus necessitating the need for clearly defined processes and practices for effective Document Analysis.

This article will discuss the potential of LLMs in extracting insights from unstructured data, including the steps involved for effective analysis.

What are Large Language Models (LLMs)?

Large language models are a type of artificial intelligence system that uses deep learning techniques and a large corpus of training data to understand and generate new data. This gives them a wide range of use cases, including translation, sentiment analysis, text classification, and even document analysis.

It might be interesting for you: Not only GPT. What LLMs can you choose from?

How to get insights from unstructured text with LLMs?

Before analyzing any text document to extract insights, there are a few steps you have to follow to ensure effective analysis and mitigate errors. The steps include:

Define your goals

What’s your goal, and what kind of insights do you want to generate from the unstructured text? Defining your goals can be as simple as identifying the sentiment, topics, keywords, and relationships in the text. You may also want to classify, summarize, or generate new text based on what you’re working with to make the process easier.

Depending on the type of document you’re working with and your goals, you may also need different tools and NLP techniques to achieve your desired goals. You also need to define the intended audience to make the output more understandable. For instance, when analyzing medical records, medical jargon may be useful to a doctor but hardly comprehensible to a patient. Therefore, defining your intended audience helps the model articulate its response in a more discernible manner.

Data collection

Data collection in document analysis typically involves putting together the various documents that need to be analyzed. Some LLMs in document analysis are more effective than others when analyzing multiple documents, so you may need to analyze them as single or multiple documents depending on the model you’re working with.

Data preprocessing

For effective analytics, you need to remove noise and irrelevant information from the text document. Data preprocessing involves tasks such as normalization, tokenization, stop-word removal, stemming, and sentence segmentation.

The former, normalization and tokenization, are crucial elements of the data preprocessing process. [2][3] Take tokenization, for instance. While longer sentences are generally considered more important in most documents, shorter sentences, like in medical and financial records, may carry more significance.

You may also need to perform sentence and word embeddings to facilitate efficient NLP summarization – which is a crucial element in document analysis. Sentence and word embeddings help represent the text in a vector/mathematical format, which can be easily processed by LLMs and NLP models.

Text representation

For effective analytics, you need to represent the text data in a format that LLMs can easily process. This may involve performing tasks such as word embedding, vectorization, and sentence embedding. These data representation techniques help to map the text data in the document into numerical formats that can be used to perform analytics and calculations.

LLM model selection

While some of the most popular LLM models like BERT, GPT-3, GPT-4, and other transformer-based models help generate useful insights from text data, it is important to remember that there are other models that may do a great job without requiring as much computing power.

For instance, the models mentioned above generate insights from unstructured text data through extractive summarization by clustering output embeddings from their training data. Conversely, models that use sentence scoring methods utilize information theory to assign each sentence in the input document with a score based on relative frequencies. [4] Typically, a high-value score means that the sentence is highly informative. When leveraged correctly, these models may provide better insights from unstructured data.

LLM training

While this step may not be vital for ‘regular’ documents that aren’t domain-specific, it may be necessary for some niche documents like medical records. With regard to document analysis, LLM training involves feeding the text data in the source document to the model and fine-tuning its parameters to enable it to learn the patterns and relationships in the text. This way, the document is better able to produce a factual analysis.

Text analysis

After preparing your data and choosing an appropriate LLM in document analysis, you can now apply the text to the model so that it can analyze the data and generate insights. The fact that LLMs are trained on vast amounts of training data means they can generate more accurate and nuanced insights by leveraging their training data. LLMs in document analysis can be used for a wide variety of tasks such as topic modeling, named entity recognition, and question answering.

Evaluation

As mentioned earlier, LLMs are prone to errors such as model hallucinations. Therefore, it is important to evaluate the quality of insights generated by the model. You can do this manually by comparing the insights to figures and facts mentioned in the original document and assessing their relevance and accuracy. Alternatively, you can use automated text evaluation metrics like ROUGE, perplexity, or F1 score to evaluate the quality of the insights.

Visualization

Visualization helps the insights become more accessible and easily understandable. The process typically involves presenting the insights in a clear, concise, and visually appealing manner. Depending on the nature of the insights, you can use various visualization techniques, including word clouds, charts, and graphs.

Read more about How AI is revolutionizing document analysis

Final thoughts

LLMs in document analysis offer groundbreaking solutions for businesses looking to leverage vast amounts of unstructured data to generate insights. By integrating the insights into your business model, you are better able to make informed decisions, create value, and solve business problems more efficiently and effectively. For instance, insights generated by LLMs can help businesses enhance their customer service, product development, marketing, and much more.

References

[1] Forbes.com. What is Unstructured Data and Why is it So Important to Businesses. URL: http://bit.ly/3Kh4f7g. Accessed July 20, 2023
[2] Saturncloud.io. Normalization in Data Processing. URL: https://t.ly/7Z9cf. Accessed July 20, 2023
[3] Oreilly.com. Processing Data Using Tokenization. URL: https://t.ly/OdJmb. Accessed July 20, 2023
[4] Towardsdatascience.com. Sentence Scoring. URL: https://t.ly/nDHx2. Accessed July 20, 2023

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