Learnings from Data Integration for Augmented Language Models
This work provides insights for researchers and developers working on enhancing LLMs with data integration capabilities, but it is incremental as it applies existing data integration concepts rather than introducing new methods.
The paper addresses the limitation of large language models lacking access to up-to-date, proprietary, or personal data by exploring how lessons from data integration research can inform efforts to extend LLMs with external data access techniques.
One of the limitations of large language models is that they do not have access to up-to-date, proprietary or personal data. As a result, there are multiple efforts to extend language models with techniques for accessing external data. In that sense, LLMs share the vision of data integration systems whose goal is to provide seamless access to a large collection of heterogeneous data sources. While the details and the techniques of LLMs differ greatly from those of data integration, this paper shows that some of the lessons learned from research on data integration can elucidate the research path we are conducting today on language models.