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Large Language Models: Unstructured data’s new best friend!

In our most recent blog post, we showcased how AI-enabled Search is helping organisations to uncover and access business-critical information quickly and clearly. In this blog post, we dive deeper and explore Large Language Models and their role in AI-Enabled Search.

In the rapidly evolving landscape of artificial intelligence, Large Language Models have become pivotal in redefining how we interact with information and unstructured data. In their 2023 Hype Cycle for Generative AI, Gartner estimates that more than 80% of organisations will implement generative AI solutions by 2026.

According to TechRepublic, by 2023, IDG projects that there will be 163 zettabytes of data in the world, and estimates indicate that 80% of this data is unstructured. This includes various forms of data like emails, documents, policies, social media posts and more, which do not fit into traditional database formats.

This prevalence is even more striking when considering the insights of Bernard Marr, a futurist and data technology expert, who notes that up to 90% of the data being generated daily is unstructured. The volume of this data is expanding rapidly, growing at a rate of 55 to 65% per year. This growth signifies a vast reservoir of potentially valuable information that, until recently, remained largely untapped or underutilised in business contexts.

Supporting these findings, Gartner, a leading research and advisory company, also estimates that around 80% of business data is unstructured, with some estimates going even higher. This unstructured data is produced not only by humans in the form of text messages, emails, and documents but also by automated systems and devices, creating a diverse and voluminous data landscape.

These statistics highlight the critical importance of technologies like Large Language Models (LLMs) and other AI-enabled Search tools in managing, analysing and deriving value from the vast amounts of unstructured text in the business world. Their ability to process, understand, and extract insights from such data is crucial for businesses seeking to fully leverage their data assets.

What are Large Language Models?

AI encompasses a range of technologies that enable machines to perform tasks that typically require human intelligence. One of the most powerful AI applications in recent times is the use of large language models such as GPT-4-turbo, Claude, and LLama. The use of Open-Source models like Falcon has become popular in areas like Insurance and Law, where it’s possible to fine-tune the LLM’s language based on a customer corpus of text – essentially becoming more specialised within that particular field.

Large language models excel at understanding and generating human-like language. This specific Machine Learning Model reaps the benefits associated with the Transformer Architecture, which, in conjunction with a massive amount of data, is capable of learning intricate patterns, nuances, and relationships in language, making it proficient in Natural Language Processing.

The “Large” in “Large Language Model” stems from the relationship which the model has to its training data. Large Language Models are trained using vast amounts of text data to learn patterns, context, and relationships in language. This training allows the model to perform various language-related tasks, such as answering questions, completing sentences, generating creative content, and more.

These models have revolutionised natural language processing and understanding, allowing them to generate coherent and contextually relevant responses to diverse prompts. In the context of Language Models, a prompt is a concise instruction or input given to generate a specific language-based model response.

The use cases for using LLM technology are endless. Individuals and businesses now use them in daily workflows and for extensive research pieces. For example, businesses searching through large datasets and archives for past information to generate new content, bids and policy docs. If we dive into the example of using LLMs and AI-enabled Search for bid creation we can see the huge benefits in being able to quickly pull together all the relevant information to make a thorough and swift response to tender applications – easing workload and creating more opportunities to win and grow as a business.

Transforming Search Capabilities

  • Enhanced Query Understanding

Traditional search engines often rely on keyword matching, which can miss the context or intent behind a query. LLMs, in conjunction with state-of-the-art embedding models, provide more relevant and context-aware results. This leap in understanding is invaluable for businesses seeking precise information from large data troves.

  • Natural Language Search Interfaces

Users can now interact with search engines using conversational language, making search more accessible and intuitive. This feature is particularly beneficial for non-technical users who might struggle with formulating complex query syntax.

  • Entity Extraction

LLMs have the ability to extract answers and entities directly from documents and data. This feature streamlines the search process, providing users with immediate, concise, and relevant information directly within the search results. An example might be where an LLM tool has been implemented within a government body responsible for tracking trends related to homelessness. Entity extraction will provide the user with a seamless insight into these statistics and related contents.

  • Multimodal Search

Language Models facilitate the integration of multimodal elements, such as images and videos, into the search process. This allows users to conduct searches not only through text but also through visual cues, further enriching the search experience and accommodating diverse forms of content. Let’s look at Amazon for example, especially for products like blue jeans. When searching for blue jeans, customers can use images such as a photo of a desired style or a screenshot from social media, as well as text descriptions to refine their search. This approach allows Amazon’s search algorithm to understand not only the textual attributes like size, colour or brand but also the nuanced visual aspects of the jeans, such as the cut, fit, and specific wash.

Challenges and Considerations

Integrating Large Language Models (LLMs) into business operations presents several challenges and considerations. One of the primary concerns is data privacy and security, especially when dealing with sensitive customer information or proprietary business data. Businesses must ensure that their use of LLMs complies with data protection regulations like GDPR and HIPAA. Another significant challenge is the potential for biased or inaccurate outputs, as LLMs can inadvertently perpetuate biases present in their training data. This requires businesses to regularly monitor and audit the model’s performance and outputs. Additionally, there’s the need for contextual understanding and domain expertise. While LLMs are proficient in generating human-like text, they may lack industry-specific knowledge or fail to grasp the nuances of certain business contexts, necessitating oversight and input from human experts.

Businesses must consider the integration and scalability of these models into their existing IT infrastructure, ensuring they can handle large-scale operations and data flows without compromising performance or efficiency. These considerations are crucial for businesses aiming to leverage the benefits of LLMs while mitigating risks and ensuring ethical, responsible use.

There are also significant environmental challenges. These models demand considerable energy, often sourced from non-renewable means, leading to high carbon emissions and contributing to e-waste through rapidly obsolescing hardware like GPUs. To mitigate these impacts, efforts are underway to develop energy-efficient algorithms and hardware, such as AI accelerators and ASICs, and to transition to renewable energy in data centres. Complementing these are model compression techniques, reducing LLMs’ size and energy needs, and the adoption of responsible AI practices, including AI ethics guidelines and environmental impact assessments, to ensure AI’s benefits don’t compromise environmental sustainability.

Adopting LLMs

The integration of large language models into AI-enabled Search is a significant milestone in the journey of artificial intelligence. For businesses, this means more efficient data handling, enhanced decision-making capabilities, and a competitive edge in a data-driven world. As we continue to harness the power of AI, it’s clear that LLMs will play a crucial role in shaping the future of business intelligence and data analytics.

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Analytics Engines are experts in AI-enabled Search, AI Strategy, Data Science, Data Engineering and Delivery Enablement. We are also experts in business. In people. In progress.

We know a thing or two about LLMs.

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by P Spence

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