Harnessing Language Models for Knowledge Base Creation

Large Language Models (LLMs) have changed how we interact with computers and understand language. They can process and generate human-like text, which opens up many potential uses. One of their key applications is creating Knowledge Bases (KBs), which are organized stores of information. These bases help computers retrieve knowledge and make inferences about various topics.

#What are Knowledge Bases?

Knowledge Bases are collections of information structured in a way that makes it easy for machines to find and use that information. They can be very useful for tasks like answering questions, providing relevant data, or supporting decision-making. However, building these bases by hand can be slow and difficult. That's where LLMs come in; they help automate the process of building and updating KBs.

#How Do Large Language Models Help?

With LLMs like Llama 2 and StableBeluga, we can draw on vast amounts of data, particularly from resources like Wikipedia. These models have a wealth of language and factual knowledge, making them great tools for identifying entities, extracting relationships, and representing knowledge.

Using LLMs can simplify and speed up the building of Knowledge Bases. Instead of relying solely on manual efforts, we can leverage LLMs to understand relationships between entities and gather information more efficiently.

#The Role of Wikipedia

Wikipedia is one of the most extensive sources of human knowledge available online. It covers countless subjects and provides a great foundation for constructing Knowledge Bases. By utilizing Wikipedia data, we can ensure a broader understanding of different topics, leading to more comprehensive Knowledge Bases.

#Fine-tuning Large Language Models

To effectively use LLMs for Knowledge Base construction, we need to fine-tune them properly. Traditional methods can be inefficient and require a lot of computer power. New techniques like Low-Rank Adaptation (LoRA) have shown promise in making fine-tuning more efficient, requiring less computational power while maintaining the performance of the models.

By effectively fine-tuning LLMs, researchers can maximize their capabilities in constructing Knowledge Bases and improve their ability to generate useful information.

#Our System: LLM2KB

The LLM2KB system is designed specifically to create Knowledge Bases using large language models. It focuses on using Llama 2 and StableBeluga models with data from Wikipedia. The process involves tuning the models to respond accurately to specific instructions and questions.

#Instructions and Training

To train LLM2KB, we generate instruction sets that help the models learn how to answer questions about different subjects. This is done through a combination of training samples that prepare the models to identify relevant object entities related to a given subject.

The instruction tuning allows the models to understand the context better, which leads to more accurate answers.

#Processing Data

When we build our Knowledge Base, we start by looking for relevant Wikipedia pages based on the subject entity we are working with. Using a technique called Dense Passage Retrieval (DPR), we can quickly find and retrieve relevant information.

After identifying the relevant pages, we chunk the text to fit within the limits of the model while still keeping the context intact. This helps ensure that our models can process the information effectively and generate accurate responses.

#Challenges Faced

While the LLM2KB system is designed to automate the process of building Knowledge Bases, several challenges remain. Some of the issues we've encountered include:

• Prompt Sensitivity: LLMs can be quite sensitive to changes in how questions are asked, which can affect their performance.

• Hallucination: This refers to a situation where the model generates answers that sound plausible but are actually incorrect or made up.

• Entity Recognition: Sometimes, even if the model generates a correct answer, the querying systems might not return relevant information that matches the generated entities.

#Results Achieved

Through our experimentation with the LLM2KB system, we observed notable results in terms of precision, recall, and overall quality of the Knowledge Base created.

The system was evaluated using different methods of generating training samples, which helped us identify the most effective approach. We found that the configuration and instructions given to the models significantly influenced their ability to provide accurate responses.

Each relation tested in our models showed differing levels of performance, with some relations scoring higher than others. For example, certain relations that required numerical answers, like how many children a person has, performed poorly. This reflects the limited context provided by Wikipedia on such specific topics.

#Future Directions

Given the successes and challenges we experienced, there are several avenues for future development. We plan to experiment with larger language model versions to see if their increased capacity can further enhance performance.

Additionally, we want to investigate techniques that encourage models to follow a chain of thought when forming responses. This may help improve the overall accuracy and reliability of the answers provided by our system.

#Conclusion

The integration of large language models into the construction of Knowledge Bases presents exciting possibilities. The LLM2KB system demonstrates how effective these models can be in automating knowledge retrieval and representation while addressing complexities associated with this task.

By leveraging LLMs and existing resources like Wikipedia, we can simplify the process of building comprehensive Knowledge Bases, paving the way for improved information retrieval and understanding in various applications. Through ongoing research and development, we hope to refine these methods further, ensuring that machines can effectively use and contribute to the wealth of human knowledge available today.