Large Language Models (LLMs) are AI tools that can understand and generate human language. These models have been trained on massive amounts of text data, which gives them a deep understanding of human language’s structure and meaning. LLMs are capable of performing various language tasks such as translation, sentiment analysis, and chatbot conversation.
On the other hand, Knowledge Graphs are databases that represent and connect data and information about different entities. They consist of nodes representing objects, people, or places, and edges defining the relationships between these nodes. Knowledge Graphs help machines understand how entities are related to each other and draw connections between different things.
LLMs have some limitations, as they are often considered “black boxes” because it’s hard to understand how they arrive at conclusions. They also struggle with retrieving factual information accurately. This is where knowledge graphs come in. By providing external knowledge, knowledge graphs can help LLMs in their inference process.
To make the most of their strengths, LLMs and knowledge graphs can be combined in three different ways:
1. KG-enhanced LLMs: In this approach, knowledge graphs are integrated into LLMs during training to improve comprehension.
2. LLM-augmented KGs: LLMs can enhance various KG tasks like embedding, completion, and question-answering by analyzing textual data within KGs.
3. Synergized LLMs + KGs: LLMs and KGs work together, enhancing each other for two-way reasoning driven by data and knowledge.
Researchers are actively exploring these approaches to leverage the capabilities of LLMs and KGs together. By combining textual data with a knowledge graph, it becomes easier to retrieve information and answer complex questions that span multiple documents. The structured representation provided by knowledge graphs also makes it easier to store and connect structured and unstructured data, improving information retrieval.
The collaboration between LLMs and KGs holds promising possibilities for various applications, including enhancing transparency and interpretability, multi-hop question answering, and combining textual and structured data. As technology advances, this integration has the potential to drive innovation in fields like search engines, recommender systems, and AI assistants, benefiting both users and developers alike.
References:
– Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi
– Project Management by Humans (Sponsored)