Autoregressive Language Models for Knowledge Base Population: A case study in the space mission domain
This work addresses the challenge of efficiently populating and maintaining knowledge bases for organizations, offering a more affordable and lower-cost inference solution, though it is incremental as it applies existing fine-tuning methods to a specific domain.
The paper tackles the problem of knowledge base population (KBP) by fine-tuning autoregressive language models for end-to-end KBP, specifically in the space mission domain, achieving competitive or higher accuracy than larger models with smaller, specialized models.
Knowledge base population KBP plays a crucial role in populating and maintaining knowledge bases up-to-date in organizations by leveraging domain corpora. Motivated by the increasingly large context windows supported by large language models, we propose to fine-tune an autoregressive language model for end-toend KPB. Our case study involves the population of a space mission knowledge graph. To fine-tune the model we generate a dataset for end-to-end KBP tapping into existing domain resources. Our case study shows that fine-tuned language models of limited size can achieve competitive and even higher accuracy than larger models in the KBP task. Smaller models specialized for KBP offer affordable deployment and lower-cost inference. Moreover, KBP specialist models do not require the ontology to be included in the prompt, allowing for more space in the context for additional input text or output serialization.