Today’s data landscape is immense, with IDC’s Global DataSphere predicting exponential growth each year.
Yet, despite advanced tech, businesses still struggle to locate essential information when needed.
This happens because most data remains unstructured and, therefore, hard to manage. Intelligent document processing (IDP) systems often fall short by either taking too long to deploy or failing to produce usable, accessible results.
Enter Grooper 2024, our IDP platform that doesn’t just “archive” data but transforms it for actionable insights. Grooper can do this thanks to powerful Large Language Model (LLM) integrations.
Here’s a closer look at how Grooper’s LLM integration can elevate your document processes and help you customize LLMs for tailored precision in data extraction.
Transform your data and document processing tasks with a seamless integration of AI and LLMs.
See how Grooper equips you to easily test different models and fine tune them for best results. Watch how you can deploy LLMs on platforms like AWS, Azure, or your own machine. You will also discover how to use advanced OCR to get clean data for your LLMs. GET THE VIDEO:
LLM fine-tuning is the process of taking a pre-trained model and running it through further training on smaller, specific datasets in order to improve accuracy on a specific domain or task.
Fine-tuning a model is preferable to building one from the ground up, as the benefits are reduced costs, time, and reduced work as you can use new, advanced models.
Fine-tuning an LLM makes the model work better for natural language processing in areas like:
For very specific tasks such as these, tailoring a model for a particular domain is vital.
As I have said earlier, quality data plays a significant role in impacting your model's performance. The old saying "Garbage in, garbage out" is very true when it comes to large language models.
But IDP solutions like Grooper can transform dirty document images with noise, distortion, and handwriting into nearly perfect, clean electronic data. Any missing data can be flagged for a human operator to complete.
That data can then be leveraged in an LLM model to deliver great results.
These variables are key to adjust and determine the best configuration for your LLM projects. Clean data helps the model perform well, even if it is unseen data to avoid the problem of overfitting.
Using a pre-trained model for fine-tuning large language models is critically important. Find a pre-trained model that matches the requirements of your tasks results in less fine-tuning and better model performance in specialized tasks.
During the fine-tuning process:
Once fine-tuning is complete:
By consistently evaluating and validating the model, you can optimize its performance and ensure it generates accurate and relevant outputs for your specific needs.
Creating data to fine-tune LLMs is easy with solutions such as Grooper. Simply:
1. Configure an OpenAI LLM provider with a key supplied by OpenAI (see our wiki for details).
2. Create an “AI Extract” Fill Method in Grooper, which requires at least Grooper 2024.
3. Select a fully indexed Grooper batch that used the AI Extract Fill Method in Step 2. Or index a batch fully, making sure to correct all errors.
4. Right-click on the node in Grooper Design Studio where the AI Extract Fill Method is set up (data model, section, or table).
Then fill in the details and select the batch of documents you want to use.
5. Right-click again and choose “Start Fine Tuning Job” from the menu.
Then, choose a base model. (Only certain models can be used for fine tuning.) Add a name suffix to the model.
6. Execute fine-tuning job.
7. Select your newly created fine-tuned model from the dropdown in the AI Extract Fill Method you set up in step 2.
8. Voila! Your fine-tuned model is now being used.
The model will take some time to be available from OpenAI. You will get an email notification from OpenAI when the model is created.
Note that the model is only available to the API key that was used to create the fine-tuned model. Also know that when you select models to fine tune, you will see your fine-tuned models here as well. You can continue to fine tune models with new data.
Tests with you new model should show immediate results. You can use this model anywhere you would normally use a fine-tuned model, not just in Grooper.
The model will show in your OpenAI Dashboard under the fine-tuning section. You can also see the progress of your fine-tuning jobs in this dashboard.
Fine-tuning LLMs is a supervised learning process where you use a dataset of labeled examples to update the model's weights and improve its ability for specific tasks.
Let's learn about some of the top methods for fine-tuning LLMs.
This method is commonly used to tailor LLMs for specific applications like text classification or named entity recognition.
This approach is particularly useful when task-specific data is scarce or expensive, making it a valuable tool for fine-tuning LLMs.
This method is particularly useful when the task-specific dataset is large and significantly different from the pre-training data. By allowing the entire model to learn from the task-specific data, full fine-tuning can lead to improved performance on the target task.
By fine-tuning a pre-trained model on task-specific data, we can significantly reduce training time and improve accuracy. This approach is particularly useful when dealing with limited task-specific data.
PEFT mitigates these challenges by updating only a much smaller number of parameters, effectively "freezing" the rest of the model. This strategy significantly reduces memory requirements, allowing for efficient training on limited hardware.
By preserving the original LLM's weights, PEFT prevents catastrophic forgetting, ensuring that the model retains previously learned information. This is particularly beneficial when fine-tuning for multiple tasks, as it avoids the storage problem associated with creating multiple copies of the full model.
Various PEFT techniques, such as Low-Rank Adaptation (LoRA) and Quantized-Low-Rank Adaptation (QLoRA), have been developed to further optimize the training process and improve performance.
The model learns to share knowledge and representations across different tasks, reducing the risk of overfitting and improving its ability to generalize to new, unseen data. However, multi-task learning requires lots of labeled data for each task, which can be tough to get in certain scenarios.
This approach is particularly useful when we want to optimize the model's performance for a well-defined task like:
While task-specific fine-tuning can lead to impressive performance gains, it's important to remember the potential for catastrophic forgetting, where the model may lose knowledge acquired during pre-training.
To prevent this, careful tuning and regularization techniques can be employed to balance the model's ability to learn new information while also preserving its existing knowledge.
Here’s why this capability is so powerful:
Essentially, Grooper’s ability to convert any document batch into data for fine-tuning empowers you to make models that are truly tailored to your needs, without requiring specialized technical skills or a big budget.
This not only boosts accuracy but also makes your AI solutions adaptable and sustainable over time.
While large language models like OpenAI’s GPT models, Meta’s Llama, Microsoft’s Phi, Google's Gemini, and many others bring innovative ways to process language, Grooper 2024 takes this further by enabling seamless testing across LLMs. You can select the model that aligns best with your unique documents and workflows, whether that's through OpenAI’s API or models deployed in Microsoft Azure.
This is crucial because each large language model has unique strengths and weaknesses. Grooper’s LLM integration allows you to set up multiple LLM connections, giving you access to any model that follows the OpenAI API structure or is available on Azure.
This flexibility means Grooper’s LLM testing isn’t limited to just one provider. You’re free to explore and compare performance across hundreds of models.
Here are 3 advantages to LLM fine-tuning with Grooper:
Grooper’s superior OCR extracts near-perfect text, setting a solid foundation for feeding this structured data into an LLM test bed. In Grooper, this process is simplified: fields like “First Name” or “Account Number” can be added to your data model.
Grooper then automatically builds the LLM prompt for you. No complex setup or prompt engineering needed.
This feature is currently available for OpenAI models, but the generated data files are compatible with most LLMs for fine-tuning.
Fine-tuning LLMs with real, domain-specific data improves accuracy significantly. This is compared to fine tuning with generic training data or synthetic data, which studies show may reduce model quality.
Imagine using a model fine-tuned on actual invoices from your accounts payables, or on real academic transcripts for educational applications. Grooper now lets you fine-tune any OpenAI model with the data you process, empowering you to create models that outperform generic LLMs in targeted extraction tasks.
This is especially valuable as it enhances long-term solution performance, ensuring your IDP system continually improves with each training cycle.
Whether in the cloud or hosted locally, Grooper’s flexibility means you can deploy open-source models or integrate with Azure and OpenAI hosted options without needing specialized IT staff.
Testing and tuning becomes a straightforward, single-click process, turning Grooper into a fully equipped fine-tuning workbench for IDP. This approach removes complexity, helping you focus on improving model performance rather than managing technical challenges.
Imagine the model as a knowledgeable assistant who already understands a wide range of topics. It’s already well versed in a huge variety of sources like books, websites, and general information.
However, if you want this assistant to be especially skilled at handling tasks specific to your department, you can "fine-tune" it. Fine tuning is performed by training it with data directly relevant to your businesses' specific needs.
For example, if your department deals with specific industry regulations or unique workflows, you’d provide it with examples and information focused on those areas. This additional training enables the model to become more accurate and responsive to questions and tasks related to your field.
In short, fine-tuning takes a broadly trained tool and sharpens it's expertise. This enables it to perform more effectively within your department’s specific context while still retaining it's general knowledge.
A general model might answer correctly 7 or 8 times out of 10 for your specific needs. But a fine-tuned model can often improve that accuracy closer to 9 or even 10 times out of 10.
This means fewer misunderstandings, more precise responses, and ultimately, better support for your department’s work.
So, while fine-tuning doesn’t make the model perfect, it often brings it much closer to the mark when it comes to the specific tasks or topics that matter most to you.
Grooper’s 2024 release has created a one-of-a-kind platform that lets you leverage the best of both technologies: exceptional text extraction with Grooper’s OCR and the transformative power of LLMs for unstructured data.
With Grooper, your organization can finally unlock the potential of LLMs to make data not just storable, but searchable and actionable.
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We are often asked about the difference between all these new AI technologies. We at BIS been at the forefront of the LLM AI revolution and were an early adopter of OpenAI's flagship GPT models.
That being said, there's a lot of information buried in these acronyms. So let me spend a little time explaining the terms and outlining where each fits.
In IDP and document technologies, this means we're teaching the LLM about our documents. LLMs only understand text (we'll ignore "multi-modal" models right now).
RAG stands for Retrieval-Augmented Generation. That means the LLM is fed some data that's been "retrieved" first before the prompt is responded to.
Sounds a lot line fine-tuning, eh?
The difference between the two is that the RAG information is not added to the model's training. It's just a knowledge base that's used to help the LLM answer the questions (prompts) more accurately.
RAG systems use external data. For instance, when Grooper first implemented GPT extractors, we implemented them in a RAG method. This is because Grooper hands the text from the documents in scope to the LLM with the prompt. That textual data is used to respond to the prompt.
However, RAG is much more powerful than just an initial implementation. RAG can be used to connect to any data source and use retrieved data (think Web Services lookups, SQL database lookups, any data you can retrieve can be used) to help guide the LLM in responding to the prompt.
This has been shown to greatly reduce the LLM's tendency to "hallucinate" aka "make stuff up." RAG is also much better when data is fluid. Fine-tuning is a retraining step and implies that the training data will not change.
Fine-tuning is used more to tailor the model behavior overall instead of just for a chat session or prompt. However, both are used to get better, more accurate data from LLMs.
Grooper uses both these techniques (and a lot more) to get better results from your documents. If you haven't seen what Grooper is capable of with AI, you need to reach out to us today and get a demo.