Large language models (LLMs) are neural networks that can perform a broad range of natural language processing (NLP) tasks, such as:
Multimodal LLMs can understand multiple data types like images, text, audio and video. Consequently, LLMs can effectively capture unstructured data from PDFs, streamlining a fundamental but tedious task in business processes.
Unlike the broader category of generative AI – which encompasses a variety of models performing a range of tasks – LLMs represent a type of model built for natural language processing. For example, models like DALL-E and Stable Diffusion fall under the umbrella of generative AI, but they are not large language models (both DALL-E and Stable Diffusion are designed for image generation, not language processing).
LLMs offer several benefits to businesses looking to extract data from PDFs or images:
Humans are prone to making errors at a rate generally estimated to be around 1%. Though 1% might seem insubstantial, in tasks requiring high precision, a minute error rate can create costly mistakes if left uncorrected. In contrast, LLMs can process information more accurately than humans, especially for repetitive or data-intensive tasks.
LLMs excel in analysing large volumes of data at speeds unattainable by humans. Reviewing long documents such as annual reports and legal contracts can be time-consuming for employees. LLMs, however, can sift through these documents quickly in a fraction of the time.
The cost-effectiveness of employing LLMs is a direct consequence of their speed and accuracy. Businesses integrating LLMs into their operations can access and analyse their data more rapidly, gaining insights to drive strategic decisions. Moreover, by minimising human error, LLMs can help avoid the expenses associated with correcting mistakes, which can be substantial in financial services.
Overall, these advantages can significantly impact a company's competitiveness by enabling quicker access to reliable data and reducing the likelihood of costly errors.
Training (or pretraining) involves teaching the model to process tokens. The user implements basic tasks that enable the model to understand why tokens are structured in a specific order.
Foundational training is extensive (both in terms of time and effort) and focuses on ensuring the model comprehensively understands textual data. At the end, the model will understand the structure of natural language but will not yet be able to perform tasks.
The next stage is to train an image encoder to handle visual data. Training an image encoder involves applying a dataset with varied visual tasks for effective feature extraction from visual data.
The goal is to develop an encoder that can generate features that accurately represent the content in images. Often, you would deploy a Vision Transformer (ViT) or a similar model for this purpose.
At this point, there are two pre-trained models – the language model and the image encoder. The next step is integrating these models to use the image data effectively alongside the language model.
An alignment module – a connecting component of the multimodal model – is created to bridge the gap between the two models. The module learns to translate the rich features output from the image encoder into a format the LLM can understand. Essentially, the module ensures the LLM can decode the output of the encoder.
When using the LLM, inputs typically consist of tokens that form a query or other textual information. If an image exists, it is processed initially through the image encoder. The encoder’s output is then aligned and structured in a way that the LLM can interpret.
Both elements – the textual tokens and the processed image data – are concatenated (linked in a sequence). The alignment module is trained to align the visual information with the large language model, which is faster than the foundational learning, as there are fewer parameters to train. Although all three modules are connected, the alignment module is primarily being trained.
Finally, the combined input is fed through the LLM. The LLM processes the textual and visual information, allowing it to analyse and understand the content of the PDF comprehensively.
In summary, multimodal language models work by integrating and aligning separate models for language and images, creating a system capable of processing and understanding textual and visual data.
Choosing a commercial LLM, such as ChatGPT’s GPT-4, may seem like an obvious solution. The issue is that publicly available LLMs are not specialised. While generative AI like GPT-4 can handle a broad range of tasks, a machine learning model designed for a specific NLP function will outperform it.
Therefore, businesses should evaluate the benefits of a tailored solution. Ultimately, the choice between a versatile, general-purpose model like GPT-4 and a specialised one depends on the task's requirements and context. For functions involving accuracy and speed – such as automated PDF extraction – it is faster and more cost-effective to build a scalable pipeline for PDF extraction using a specialised model.
Evolution AI’s technology, funded by one of the largest AI R&D grants ever awarded by the UK government, offers a tailored solution to PDF extraction. To find out more, book a demo or email hello@evolution.ai.
