Unifying Large Language Models and Knowledge Graphs: A Roadmap for Content Generation

Table of content: 

1. Advantages of Complementing LLMs with KGs
2. Keeping the “Human in the Loop” in Scalable Content Production
3. Preserving Brand Tone of Voice and Intellectual Property
4. Three Steps to Setup a Generation Project
5. Conclusion

In today’s rapidly evolving digital landscape, content creation has become more crucial than ever for brands to engage their audiences. With the emergence of large language models (LLMs) such as ChatGPT and GPT4, natural language processing and artificial intelligence have seen revolutionary advances. While excelling in creative content generation, LLMs face some limitations. A key challenge lies in their ability to access and integrate factual knowledge, real world experiences and above all the brand’s core values. In addition, LLMs can sometimes produce output with hallucinated or fictitious elements, adding a layer of complexity. 

Knowledge Graphs (KGs) are crucial to overcoming limitations. They host structured, factual data and provide a solid foundation for training LLMs, ensuring that content is articulated and grounded in reliable information. This synergy represents a substantial step towards more authoritative content driven by artificial intelligence.

In addition, the knowledge graph enhances structured data, refining assumptions about content by infusing brand values into the model. Using an ontology for your brand, product-specific traits can be amplified. For example, when it comes to RayBan, specific materials take precedence. This goes beyond fact-checking by formalizing and operationalizing domain-specific insights.

This emphasizes the central role of ontology, making it clear that semantic data has a sophisticated purpose beyond mere fact-checking.

In this context, we have created a solution for SEOs and content marketers, enabling editorial teams to scale content production while maintaining maximum control over quality and relevance.

Whether product descriptions, restaurant profiles, or introductory text for category pages, our tool delivers reliable results. In this article, we introduce you to our Content Creation Tool and explain why it is so ahead of other AI content creation tools. 

Advantages of Complementing Large Language Models (LLMs) with Knowledge Graphs (KGs)

The synergy between LLMs and KGs can significantly enhance the capabilities of content generation systems, making them more accurate, reliable, and adaptable to a wide range of applications and industries. By integrating KGs with LLMs, we can leverage the advantages of both technologies. 

Indeed, integrating Knowledge Graphs into Large Language Models can help overcome some of the limitations and challenges of using Large Language Models alone, such as:

• Lack of factual knowledge and consistency, such as making errors or contradictions when dealing with factual information or common sense knowledge;
• Lack of interpretability and explainability, such as being unable to provide the source or justification of the generated outputs or decisions; 
• Lack of efficiency and scalability, such as requiring large amounts of data and computational resources to train and fine-tune the models for different tasks or domains.

One way to combine Knowledge Graphs and Large Language Models is to use the Knowledge Graph as a source of external knowledge for the Large Language Model so that it can answer questions or generate texts that require factual information. For example, suppose you ask a Large Language Model to write a biography of Leonardo da Vinci. In that case, it can use the Knowledge Graph to retrieve facts about his life, such as his birth date, occupation, inventions, artworks, etc., and use them to write a coherent and accurate text. This way, the Large Language Model can leverage the structured and rich knowledge of the Knowledge Graph to enhance its inference and interpretability.

This synergy between LLM and KG opens up new possibilities for content generation and reasoning, such as:

• It generates more informative, diverse, and coherent texts incorporating relevant KG knowledge, such as facts, entities, relationships, etc.
• It generates more personalized and engaging texts that adapt to user preferences, interests, and goals, which KGs can shape.
• It generates more creative and novel texts that explore new combinations of knowledge from KGs, such as stories, poems, jokes, etc.
• It can store newly generated content and effectively re-use archival content. A KG acts as a long-term memory and helps us differentiate the content we produce.

LLMs and KGs can work together to enhance various content-generation applications. For instance, in question answering, they can generate accurate, concise, and comprehensive answers by using information from KGs in conjunction with context from LLMs. In dialogue systems, they can produce relevant, consistent, and informative responses by leveraging dialogue history from LLMs along with user profiles from KGs. Additionally, they can generate faithful, concise, and salient summaries for text summarization by utilizing input text from LLMs alongside key information from KGs. In constructing AI agents in SEO, they can teach how to answer questions instead of predicting similar sentences. 

Keeping the “Human in the Loop” in Scalable Content Production

At WordLift, we advocate the crucial role of human oversight and control, especially when content production reaches thousands of pieces. 

Our approach goes beyond simple automation, focusing on meticulous modeling of the data within the Knowledge Graph (KG) and curating and refining the underlying ontology. By identifying the essential attributes used to generate dynamic prompts, we enable companies to train custom language models to maintain a firm grasp on the quality and relevance of their content while meeting rigorous editorial standards.

Tony Seale – post on Linkedin

In our pioneering approach, we’re stepping into a critical battleground between content creators and AI tools. The current landscape is inundated with subpar content churned out by these tools, threatening the deal between search engines and content creators. 

This.

All of this is the direct result of people throwing too much (AI) 💩 at the wall and Google having to wash it off. https://t.co/L0UXO55jHl

— Joost de Valk (@jdevalk) October 5, 2023

Our innovative strategy directly addresses this contentious issue surrounding generative AI and content creation. Furthermore, our KG-centric methodology is a game-changer. It liberates companies from relying on external data, as it ensures that internal sources suffice for robust language model training. This reflects our dedication to sustainability and underscores the ethical use of AI resources. 

In addition, we uphold the implementation of validation rules, adding an extra layer of assurance for precision and error prevention. This comprehensive approach seamlessly marries the potential of AI with the human touch, culminating in content excellence, fortified editorial control, and eco-conscious practices.

In practice, we’re producing an impressive content volume catering to some of the world’s foremost fashion brands and publishers. The true challenge isn’t merely ramping up content creation but ensuring meticulous validation of each piece. To date, we’re glad to share that we’ve achieved +500 completions per minute. This achievement exemplifies our unwavering commitment to precision and quality in content generation. 

There are clients who have approached not one but up to three agencies for content creation using AI before partnering with us at WordLift. This proves that our advanced workflow of content creation from the KG and a dynamic prompt that is built on the basis of the brand’s data and needs, is the cutting-edge solution for companies, giving them peace of mind and security. 

Preserving Brand Tone of Voice and Intellectual Property

At WordLift, we are committed to staying at the forefront of content generation by incorporating the latest advances in AI technology. In 2023, Google introduced the helpful content system update, a series of updates that somewhat condemn the indiscriminate use of AI in creating content of little value and impact to people. What Google has repeatedly emphasized as the problem is not the tool used to create content but its quality, such that it is clearly “written for people.” 

These updates align perfectly with our commitment to ethical AI, a key goal in developing our innovative content generation system at scale. Our approach goes beyond automation; we employ refined models to preserve your brand’s unique tone of voice (TOV) while safeguarding potential intellectual property (IP) issues. This process significantly elevates the quality and relevance of AI-generated content.

By setting specific validation rules within our generation flow, we can proactively detect and correct instances where the template may inadvertently quote people or brands without the appropriate rights. Moreover, our system integrates advanced fact-checking capabilities, as detailed in our article on AI-powered fact-checking, to ensure the accuracy and credibility of the information presented. This ensures that the content you generate is in line with your brand guidelines and meets legal requirements.

With WordLift’s content generation workflows, you can be confident that your content will consistently resonate with your audience, embodying your brand identity and values. We are committed to pushing the boundaries of ethical AI to provide you with content solutions that are effective and responsible.

Three Steps to Setup a Generation Project

Our user-friendly dashboard provides a seamless experience for setting up a generation project tailored to various use cases. Whether it’s introductory text, product descriptions, or restaurant content, our three-step process simplifies the setup:

1. Data Source: define the project name, select the knowledge graph you want to use, and select whether you want to use a customized or present template. To extract the data, you will use a GraphQL query. 
2. Customize the Prompt: Set the attributes and parameters that will be used to generate dynamic prompts. This lets you control and align the generated content with your brand’s messaging.
3. Validate and Refine: Establish content validation rules and review the generated content to ensure it meets your quality standards. Continuously refine the AI system’s rules to improve accuracy and relevance.

Discover how to use our Content Generation to generate high-quality content tailored to your enterprise’s specific needs.

After completing all the steps, you can save the project and initiate the generation process. The generated completions undergo the following processing and categorization:

• Valid: This status signifies that the completions have successfully passed the validation process based on the rules you established earlier.
• Warning: This status is assigned to generations that have adhered to ‘recommended’ rules but fall short of meeting ‘required’ ones.
• Error: This status is assigned when validation errors arise due to missing words or attributes you specified for inclusion. These incomplete completions can be regenerated automatically or rewritten and approved manually.
• Accepted: This status applies to all generations you have reviewed and confirmed as satisfactory.

Conclusion

The unification of LLM and KG presents a promising roadmap for content generation. Leveraging both technologies’ strengths, WordLift enables brands to create engaging and informative content at scale. With our user-centric approach and refined templates, we ensure the preservation of brand TOV and compliance with intellectual property regulations while leveraging AI and cutting-edge technologies. 

This tool isn’t available to everyone yet, but it’s available to a select group of clients. There are many tools that promise to produce content on a large scale, but there are no others on the market that are able to validate that same content in a way that corresponds to the characteristics of the brand. So if you want to know more, please contact us.

More frequently asked questions

How to do quality assurance when dealing with LLMs in SEO?

Ensuring quality when working with Large Language Models in SEO is a top priority for WordLift. We take a multi-tiered approach to quality assurance. First, our process involves using refined models specifically trained to preserve the brand’s unique tone of voice (TOV). This helps us generate content that is perfectly aligned with brand guidelines. 
We also implement rules within our generation workflow to detect and correct instances where the template may inadvertently quote people or brands without the appropriate rights, thus protecting against potential intellectual property (IP) infringement. This meticulous approach minimizes the chances of content discrepancies and ensures that generated content maintains high standards of quality and relevance.

How to ensure originality and unique brand voice when dealing with LLMs in SEO?

Maintaining the originality and uniqueness of the brand voice is a crucial goal, achieved through refined templates that are trained on specific datasets (specifically on the Knowledge Graph) tailored to reflect the brand’s style and messaging. This process ensures that the content generated meets brand guidelines and resonates authentically with the target audience. 

By establishing rules within our generation flow, we can proactively identify and address potential originality-related issues. This means that the content produced maintains the brand’s distinct voice, providing a consistent and authentic experience for the audience. In addition, our commitment to ethical AI ensures that the content generated is effective and in line with responsible content creation practices. In this way, WordLift provides a reliable solution that maintains the integrity and individuality of your brand.

What is the AI technology WordLift uses for the content generation?

The platform we developed is model-agnostic and we actively experiment with different technologies. We directly work with both Azure and OpenAI team on fine-tuning. We work directly with Hugging Face, Open AI, and Azure and our existing clients are working with fine-tuned models that are specific for their domain.

Is our data private and safe?

Yes, ensuring the privacy and safety of client data is our top priority. We implement a robust data protection strategy that revolves around Azure – one of the most secure cloud platforms available.