In today’s business landscape, Large Language Models (LLMs) are essential tools for driving innovation, streamlining operations, and unlocking new opportunities for growth. A Large Language Model, or LLM, is an advanced AI model designed to perform Natural Language Processing (NLP) tasks, including interpreting, translating, predicting, and generating coherent, contextually relevant text. One core benefit of LLMs is the ability to quickly generate insights from a large corpus of documents while using any context provided in a prompt. However, all LLMs come with challenges that can be difficult to address without the proper expertise and technology.
Challenges
While LLMs are a powerful means of interfacing with an organization’s information, the effectiveness of LLMs is often hampered by the complexity and disorganization of the data they rely on. The challenge lies not only in processing vast amounts of information but also in ensuring that this information is accurate, relevant, and structured in a way that the models can effectively learn from. If LLMs are trained on information and data that lack those characteristics, they will produce low-quality results. Furthermore, without a model of how entities within a subject area — such as finance — relate to one another, the LLM may default to an inaccurate, generalist approach to responses based on the training data, causing it to miss relevant information and references. Finally, even in the case where these two problems are solved, there is the issue of hallucinations: the phenomena wherein an LLM will produce false or divergent answers that are unsupported by the underlying training data. Given the range of errors that can crop up when using an LLM, how can an organization prepare their LLM to be trustworthy enough for enterprise use?
Solution
This is where semantic layers come into play. A semantic layer is a standardized framework that organizes and abstracts organizational data. A semantic layer also solves the fundamental disconnect that businesses face between collecting data and turning that data into actionable information by providing standard models and a consumption architecture for handling and connecting structured and unstructured organizational data. In doing so, the specific domain knowledge and expertise of the enterprise is captured in a way that is both machine and human readable, enabling better decision-making and insight generation. This interoperability allows a semantic layer to act as the bridge between your raw data and the sophisticated analytical capabilities of LLMs by structuring the underlying data to improve the coherence and explainability of an LLM’s outputs.
Benefits of a Semantic Layer for LLMs
1: Data Quality and Accessibility
A semantic layer organizes and abstracts organizational data across formats, making it accessible for both humans and machines. Within a semantic layer, data and high quality models can be tagged for LLM training and consumption. This means training on data that is not only high-quality but also rich in contextual and conceptual relationships. This improved data accessibility accelerates the training process and enhances the model’s ability to understand and generate nuanced, informed text.
For example, consider a healthcare LLM designed to provide diagnostic suggestions based on patient symptoms. With a semantic layer, patient data, medical histories, and research articles are organized and tagged with contextual relationships, such as symptoms associated with specific conditions. This way of organizing information allows the LLM to access a rich, interconnected dataset during training and operation, enabling it to recognize subtle nuances in patient symptoms and suggest diagnoses that reflect a deeper understanding of medical conditions and their manifestations. As a result, the LLM’s suggestions are not only relevant but also grounded in a comprehensive view of available medical knowledge, demonstrating the semantic layer’s role in enhancing the quality and reliability of its outputs.
By providing a standardized framework for data interpretation, semantic layers enable LLMs to access higher-quality data, leading to improved decision-making, enhanced customer experiences, and more accurate generated content. For businesses, this means being able to leverage data assets more effectively and reduce time spent looking for accurate information. This improved data discovery both accelerates the training process and enhances the model’s ability to understand and generate nuanced text.
2: Contextual Understanding
A semantic layer is not unique in its ability to organize and make available data across formats. A data catalog or a data fabric can be an effective means of delivering high quality data to consumers and machine learning models. However, semantic layers pull away from the competition in their ability to capture heterogeneous sources of data and enrich them with semantics and contextual information. The flexible data models, standardized vocabularies, quality metadata, and business context captured as a part of a semantic layer allows for LLMs and other computer applications to understand a business domain on a foundational level.
For example, imagine a multinational corporation that utilizes an LLM to streamline its customer service. This corporation operates in various countries, each with its unique set of products, services, and customer interactions. A semantic layer can organize customer feedback, service tickets, and product descriptions, enriching this data with contextual information such as geographical location, cultural nuances, and language variations. By using this semantically rich dataset, the LLM can understand not just the explicit content of customer queries but also the implicit context, such as regional product preferences or local market trends. As a result, the LLM can provide more accurate, context-aware responses to customer inquiries, reflecting an understanding that goes beyond words to grasp the subtleties of global business operations.
When a semantic layer serves as a backbone for the LLM’s data consumption it ensures that training data is coming from trusted, high-quality sources that are enriched with domain context. This foundational context empowers LLMs to generate outputs based on a more comprehensive understanding of the subject matter. By capturing and connecting content based on business or domain meaning and value, LLMs can produce more accurate and relevant outputs, tailored to specific industry needs or knowledge domains.
3: Explainable Results
Even with high-quality data and business domain understanding, “hallucinations” are still a concern when trying to use an LLM as a trustworthy source of information. LLMs hallucinate due to many reasons, including a lack of sufficient context or specific tagging in their training data. When the data lacks robust contextual information and nuanced tagging, the LLM can have a limited understanding of the relationships between different data points. This limitation can lead to the generation of outputs that are not grounded in factual information or logical inference, as the model attempts to ‘fill in the gaps’ without a robust framework to guide its responses.
The incorporation of a semantic layer can help to cut down on the prevalence of hallucinations and improve output quality by enriching the LLM’s training environment with deeply contextualized and well-tagged data. As we have seen by now, semantic layers ensure that data is not only of high quality but also embedded with lots of contextual information and relationships between data that keep the model more grounded in reality. Furthermore, an LLM trained with the aid of a semantic layer can be prompted to include explanations of its outputs, detailing the data sources it pulled from when generating output and the contextual reasons behind the selection of these sources. This level of transparency allows users to evaluate the validity of the generated content, distinguishing between well-founded information and potential hallucinations.
Hallucinations will always remain a potential issue with LLMs due to the nature of how they creatively generate output, but semantic layers offer a way to reduce the likelihood of hallucinations by providing better training data and enhancing the trustworthiness and reliability of LLM outputs through explainability.
Conclusion
In this article, we have touched on some of the potential pitfalls of using an LLM, as well as how a semantic layer can be used in concert with an LLM to mitigate those issues and improve the quality of their output. For issues of data quality, contextual business understanding, and explainability of results, semantic layers stand out as a comprehensive solution to the most pressing challenges of LLMs. Semantic layers empower LLMs to serve not just as text generators but as sophisticated tools for knowledge discovery, decision-making, and automated reasoning. Through their components including ontologies and knowledge graphs, semantic layers enrich LLMs with the ability to understand complex relationships and concepts, paving the way for advanced applications in areas such as legal analysis, medical research, and financial forecasting. In short, integrating semantic layers with LLMs presents a strategic advantage, allowing businesses to not only overcome the challenges of data complexity, but also to maximize the full potential of AI for competitive gain while minimizing risk.
If you want to learn more about how your business can take the next step in building a semantic layer, leveraging LLMs, and developing enterprise AI, contact us to get started today!