LLMs & Knowledge Graphs
What are LLMs?
Large Language Models (LLMs) are AI tools that possess the ability to understand and generate human language. These neural networks are highly powerful, consisting of billions of parameters that have been trained on massive amounts of textual data. Due to their extensive training, LLMs have gained a deep understanding of the structure and meaning of human language.
LLMs have the capability to perform various language tasks, such as translation, sentiment analysis, and chatbot conversations. They excel at comprehending intricate textual information, recognizing entities and their connections, and producing text that is coherent and grammatically correct.
What are Knowledge Graphs?
A Knowledge Graph is essentially a database that represents and interconnects data and information about different entities. It consists of nodes that represent objects, individuals, or places, and edges that define the relationships between these nodes. By utilizing knowledge graphs, machines gain the ability to understand how entities relate to each other, share common attributes, and establish connections between various elements in the world.
Knowledge graphs have the potential to be employed in a wide range of applications, including recommending videos on platforms like YouTube, detecting insurance fraud, offering product recommendations in the retail sector, and facilitating predictive modeling.
LLMs and Knowledge Graphs
One of the main limitations of LLMs is their opaqueness, meaning it is challenging to comprehend the reasoning behind their conclusions. Additionally, LLMs often struggle to understand and retrieve factual information accurately, leading to errors and inaccuracies commonly known as hallucinations.
This is where knowledge graphs can prove beneficial by providing LLMs with external knowledge for inference. Due to the evolving and challenging nature of constructing knowledge graphs, it is advantageous to utilize LLMs and knowledge graphs together in order to maximize their respective strengths.
There are three main approaches to combine LLMs with Knowledge Graphs (KGs):
KG-enhanced LLMs:
These approaches involve integrating KGs into LLMs during training in order to enhance comprehension.
LLM-augmented KGs:
In this approach, LLMs are utilized to improve various KG tasks, such as embedding, completion, and question answering.
Synergized LLMs + KGs:
This approach involves LLMs and KGs working together, enhancing each other’s capabilities through two-way reasoning driven by both data and knowledge.
KG-Enhanced LLMs
LLMs are widely respected for their ability to excel in various language tasks by learning from extensive text data. However, they have faced criticism for generating incorrect information (hallucinations) and lacking interpretability. To address these issues, researchers propose enhancing LLMs with knowledge graphs (KGs).
KGs store structured knowledge which can be utilized to improve the LLMs’ comprehension. Some methods integrate KGs during LLM pre-training in order to aid knowledge acquisition. Other approaches utilize KGs during inference to enhance domain-specific knowledge access. KGs are also employed to interpret the reasoning and facts of LLMs, resulting in improved transparency.
LLM-Augmented KGs
Knowledge graphs (KGs) are repositories of structured information that play a crucial role in real-world applications. However, current KG methods often face challenges with incomplete data and text processing for KG construction. Researchers are exploring ways to leverage LLMs’ versatility to address tasks related to KGs.
One common approach involves using LLMs as text processors for KGs, whereby LLMs analyze textual data within KGs and enhance KG representations. Some studies also utilize LLMs to process original text data, extracting relations and entities to build KGs. Recent efforts aim to create KG prompts that facilitate the understanding of structural KGs by LLMs. This enables the direct application of LLMs to tasks like KG completion and reasoning.
Synergized LLMs + KGs
Researchers are increasingly interested in combining LLMs and KGs due to the complementary nature of these technologies. To explore this integration, a unified framework called “Synergized LLMs + KGs” has been proposed, consisting of four layers: Data, Synergized Model, Technique, and Application.
LLMs handle textual data, KGs handle structural data, and with the inclusion of multi-modal LLMs and KGs, this framework can be extended to accommodate other types of data, such as video and audio. These layers collaborate to enhance capabilities and improve performance in various applications, such as search engines, recommender systems, and AI assistants.
Some Applications of LLMs and Knowledge Graphs
Multi-Hop Question Answering
When utilizing LLMs to retrieve information from documents, the process usually involves dividing the documents into chunks and converting them into vector embeddings. However, using this approach may limit the ability to find information that spans multiple documents, which is referred to as the problem of multi-hop question answering.
This problem can be effectively addressed by employing a knowledge graph. By constructing a structured representation of the information, processing each document separately, and connecting them within a knowledge graph, it becomes easier to navigate and explore interconnected documents. This enables the LLM to answer complex questions that require multiple steps.
Combining Textual Data with a Knowledge Graph
Another advantage of utilizing a knowledge graph alongside an LLM is the ability to store both structured and unstructured data, connecting them through relationships. This integration makes information retrieval more streamlined.
For instance, a knowledge graph can be used to store the structured data of past employees of a company and the companies they started. Simultaneously, it can also house unstructured data, such as news articles mentioning the company and its employees. Such a setup allows for answering questions like “What’s the latest news about the founders of a specific company?” by starting from the node representing the company, moving to its founders, and retrieving recent articles about them.
This adaptability makes the combination of LLMs and knowledge graphs suitable for a wide range of applications, as it enables handling various data types and relationships between entities. Moreover, the graphical structure offers a clear visual representation of knowledge, making it easier for both developers and users to understand and work with.
Conclusion
The synergy between LLMs and KGs is an area of active exploration by researchers, focusing on three main approaches: KG-enhanced LLMs, LLM-augmented KGs, and Synergized LLMs + KGs. These approaches aim to leverage the strengths of both technologies in order to address various language and knowledge-related tasks.
The integration of LLMs and KGs presents promising possibilities for applications such as multi-hop question answering, combining textual and structured data, and enhancing transparency and interpretability. As technology continues to advance, this collaboration between LLMs and KGs holds tremendous potential to drive innovation in fields like search engines, recommender systems, and AI assistants, ultimately benefiting both users and developers.
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