Large language models (LLMs) play a key role in the field of AI, with the ability to automatically generate human-like fluent text, however, they also present complexity, high costs, update challenges, output inconsistencies, and audit challenges. In a predominantly English-speaking context, how to integrate LLM into multilingualism and multi-domain remains a problem. As these technologies permeate the business environment, companies need to decide whether to see them as a transitory, negligible trend or a powerful tool that must be harnessed to find an application path that is both secure and ensures maximum value from the technology.
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To address these issues, the combination with the knowledge graph can bring some significant improvements to LLM.
What is a Knowledge Graph?
Knowledge graph, with its rich information structure and powerful entity association analysis capabilities, is becoming a powerful tool for exploring complex relationship networks. Taking daily life as an example, 'apple' and 'nutrition' are two entities, and the relationship between them can be accurately expressed and visualized through the knowledge graph. When such a map is constructed, we can not only simply query basic questions such as 'what are the nutrients of apples?' but also use graph algorithms and graph data science to delve into a vast and complex web of questions, such as 'of all fruits, which provides the greatest nutritional value for people of a particular age.'" Turning entity relationships into visualizations that not only reveal facts that may have been overlooked, but also provide insights into deeper insights and further generate useful data embeddings, which has great potential and value in machine learning pipelines and large language model (LLM) applications.
Knowledge graphs work in tandem with large language models
By combining LLM with a knowledge graph, the accuracy of LLM can be improved to some extent, but how do they work together? We can see these four patterns:
Mode 1: Use LLM to build a knowledge graph
In this model, we fully tap into the natural language processing characteristics of LLM, digging deep into text data from sources such as the web and academic journals. Leverage the power of LLM to generate a (theoretically) transparent knowledge graph. Unlike the opacity of LLM, the knowledge graph can be checked, answered questions (QA), and curated. For highly regulated industries such as pharmaceuticals, knowledge graphs provide clear and definitive answers that demonstrate their indispensable value.
Mode 2: Training assistance for knowledge graphs
In the second mode, we choose a different path. Instead of training LLM on a huge universal corpus, it is directly trained using existing knowledge graphs. In this way, we can build chatbots that demonstrate excellence in our products and services without misleading illusions.
Mode 3: Enrich the interaction path of LLM
The third mode focuses on enriching LLM's input and output messages with knowledge graph data. For example, LLM alone may not be able to give an answer to a request to "showcase the latest five films from actors I like." But we can enrich LLM's tips with a film knowledge graph that explores popular films and actor relationships. Similarly, when we receive a response from LLM, we can also parse and provide deeper insights based on the knowledge graph.
Mode 4: Use knowledge graphs to create better AI
Finally, the fourth model focuses on building high-quality AI using knowledge graphs. Research by Yejen Choi and his team at the University of Washington sheds light on possible directions for this model. In its study, a secondary AI called "Critic" enriched LLM, exploring reasoning errors in its responses, and in the process generating a knowledge graph for downstream consumption by the "student" model. Encouragingly, in multiple benchmarks, the "student" model exhibited smaller, more precise characteristics than the original LLM because it never learned inaccurate facts or inconsistent answers to questions.
In the process of exploring these four modes, we see the potential synergy between knowledge graph and LLM in information retrieval and question answering, opening a new chapter in the future of intelligent technology.
In summary, combining large language models (LLM) with knowledge graphs is a proven solution to improve the accuracy, transparency and interpretability of LLM. By leveraging the clear information and expertise of the knowledge graph, LLM can be compensated for. Research and exploration in this field still has a long way to go, but this direction is undoubtedly worthy of further exploration.
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