How to Use a Knowledge Base with Large Language Models

Large language models (LLMs) are powerful tools for natural language processing (NLP) that can generate fluent and coherent text on various topics. However, they also have some limitations, such as lack of factual accuracy, domain specificity, and explainability.

To overcome these challenges, researchers have proposed various methods to integrate external knowledge sources, such as knowledge bases (KBs), with LLMs. A knowledge base is a structured collection of facts and relations about entities and concepts in a domain. By using a knowledge base with an LLM, we can improve the LLM’s ability to answer questions, generate text, and reason with knowledge.

In this blog post, we will explain the concept of a knowledge base in the context of large language models, and how to use it for different NLP tasks. We will also provide some examples of existing methods and frameworks that use a knowledge base with an LLM, and discuss their advantages and challenges.

What is a Knowledge Base?

A knowledge base is a structured representation of information about a domain, such as science, history, or sports. A knowledge base consists of entities, which are the objects or concepts in the domain, and relations, which are the connections or properties between entities. For example, in a knowledge base about movies, an entity could be a movie title, a director, or an actor, and a relation could be directed_by, starred_in, or genre_of.

A common way to store and query a knowledge base is using a graph structure, where entities are nodes and relations are edges. A graph-based knowledge base can be accessed using a query language such as SPARQL or Cypher, which allows us to retrieve facts and information from the knowledge base. For example, we can use the following SPARQL query to find the names of the actors who starred in The Matrix:

PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX dbr: <http://dbpedia.org/resource/>

SELECT ?actor
WHERE {
  dbr:The_Matrix dbo:starring ?actor .
}

The result of this query would be:

actor
http://dbpedia.org/resource/Keanu_Reeves
http://dbpedia.org/resource/Laurence_Fishburne
http://dbpedia.org/resource/Carrie-Anne_Moss
http://dbpedia.org/resource/Hugo_Weaving
...

There are many sources of knowledge bases that can be used for different domains and purposes. Some examples are:

• DBpedia: A large-scale knowledge base extracted from Wikipedia, covering various topics such as people, places, events, and organizations.
• Wikidata: A collaborative knowledge base that integrates data from various Wikimedia projects, such as Wikipedia, Wiktionary, and Wikisource.
• ConceptNet: A semantic network that represents common-sense knowledge and natural language concepts.
• Freebase: A discontinued but still widely used knowledge base that contains data from various sources, such as Wikipedia, IMDB, and MusicBrainz.
• YAGO: A high-quality knowledge base that combines data from Wikipedia, WordNet, and GeoNames.

Why Use a Knowledge Base with an LLM?

Large language models are trained on massive amounts of text data from various sources, such as books, news articles, web pages, and social media posts. They learn to generate text by predicting the next word or token given some previous context. However, this approach has some limitations when it comes to handling factual information and domain-specific knowledge. Some of these limitations are:

• LLMs may not have enough exposure to certain facts or topics during training, especially if they are rare or obscure.
• LLMs may not be able to distinguish between true and false statements in the text data they consume, leading to errors or inconsistencies in their outputs.
• LLMs may not be able to explain or justify their outputs based on the underlying knowledge or logic they use.
• LLMs may not be able to adapt to new or changing information or scenarios that require updating their knowledge.

To address these issues, researchers have proposed various methods to use a knowledge base with an LLM. By using a knowledge base with an LLM, we can achieve several benefits:

• We can enhance the LLM’s factual accuracy and consistency by providing it with reliable and up-to-date information from the knowledge base.
• We can improve the LLM’s domain specificity and relevance by providing it with relevant and contextual information from the knowledge base.
• We can increase the LLM’s explainability and transparency by providing it with evidence and reasoning from the knowledge base.
• We can enable the LLM’s adaptability and flexibility by providing it with dynamic and interactive information from the knowledge base.

How to Use a Knowledge Base with an LLM?

There are different ways to use a knowledge base with an LLM, depending on the task and the goal. Some of the common methods are:

• Knowledge retrieval: This method involves retrieving relevant facts or information from the knowledge base based on the input or the context, and using them to generate or augment the output. For example, in question answering, we can use a knowledge base to retrieve the answer to a factual question, or to provide additional information or evidence for the answer. In text generation, we can use a knowledge base to retrieve facts or concepts that are related to the topic or the genre of the text, and use them to enrich or guide the generation process.
• Knowledge injection: This method involves injecting or embedding knowledge from the knowledge base into the LLM, either during training or inference. For example, in knowledge-aware pre-training, we can use a knowledge base to augment the text data with entity and relation annotations, and train the LLM to learn both textual and knowledge representations. In knowledge-enhanced inference, we can use a knowledge base to provide additional input or context to the LLM, such as entity embeddings or relation graphs, and use them to influence or constrain the output.
• Knowledge extraction: This method involves extracting or constructing knowledge from the LLM’s output, and using it to update or expand the knowledge base. For example, in knowledge base completion, we can use an LLM to predict missing facts or relations in the knowledge base, and use them to fill in the gaps. In knowledge base construction, we can use an LLM to generate new facts or relations from natural language text, and use them to create a new knowledge base.

Examples of Using a Knowledge Base with an LLM

There are many existing methods and frameworks that use a knowledge base with an LLM for various NLP tasks. Here are some examples:

• KnowBert: A framework that injects knowledge from multiple knowledge bases into BERT, a popular LLM, by pre-training it on masked entity prediction and entity typing tasks. KnowBert can then be fine-tuned on downstream tasks such as entity linking, relation extraction, and question answering.
• RAG: A framework that retrieves relevant facts from a large-scale knowledge source such as Wikipedia using a neural retriever, and generates text using a neural generator such as BART, another popular LLM. RAG can be used for open-domain question answering and text summarization.
• KILT: A benchmark that evaluates LLMs on various knowledge-intensive NLP tasks, such as fact checking, entity linking, slot filling, and natural language inference. KILT provides a unified format for input and output across different tasks, and a large-scale knowledge source derived from Wikipedia.
• KnowledGPT: A framework that bridges LLMs with various knowledge bases, facilitating both the retrieval and storage of knowledge. The retrieval process employs the program of thought prompting, which generates search language for KBs in code format with pre-defined functions for KB operations. Besides retrieval, KnowledGPT offers the capability to store knowledge in a personalized KB, catering to individual user demands.

Frequently Asked Questions (FAQs)

Question: What is a large language model?

Answer: A large language model is a neural network model that can generate natural language text based on some input or context. A large language model is usually trained on massive amounts of text data from various sources and domains.

Question: What is a knowledge base?

Answer: A knowledge base is a structured collection of facts and relations about entities and concepts in a domain. A knowledge base can be accessed using a query language that allows us to retrieve information from it.

Question: Why use a knowledge base with a large language model?

Answer: Using a knowledge base with a large language model can improve its performance and capabilities for various natural language processing tasks. It can enhance its factual accuracy, domain specificity, explainability, and adaptability.

Question: How to use a knowledge base with a large language model?

Answer: There are different ways to use a knowledge base with a large language model, depending on the task and the goal. Some of the common methods are knowledge retrieval, knowledge injection, and knowledge extraction.

Summary

In this blog post, we explained the concept of a knowledge base in the context of large language models, and how to use it for different natural language processing tasks. We also provided some examples of existing methods and frameworks that use a knowledge base with an LLM, and discussed their advantages and challenges.

We hope this post has given you some insights into how to leverage external knowledge sources with powerful language models to enhance your NLP applications. If you have any questions or feedback, please feel free to leave a comment below.

Disclaimer: The content is for informational purposes only and should not be taken as professional advice.