Conceptual engineering is a rapidly growing field that leverages natural language processing and machine learning techniques to improve our understanding of concepts and their relationships. In this article, we explore the use of large language models (LLMs) in conceptual engineering, specifically focusing on their potential impact on society. We discuss how classification procedures can be used to evaluate and improve the alignment between natural language definitions of concepts and their representation in knowledge graphs, leading to more accurate and relevant results for users.
Alignment and Representation
The alignment between natural language definitions of concepts and their representation in knowledge graphs is a crucial aspect of conceptual engineering. By using classification procedures, we can evaluate how well the natural language definitions align with the extensions of concepts in knowledge graphs. This process can be improved by using LLMs to generate rationales for and against an entity as an element of a concept’s extension, followed by a final positive or negative answer.
Social Impact
The use of LLMs in conceptual engineering has significant implications for society. By improving the alignment between natural language definitions of concepts and their representation in knowledge graphs, we can enhance the accuracy and relevance of search results and recommendations. This can have practical applications in various fields, such as data governance and socially responsible data management. Moreover, our approach provides a new perspective on success conditions for CE, leading to ameliorative refinement of knowledge graphs as a topic for future research.
Limitation and Future Work
While our method shows promising results, there are limitations to consider. The reliance on a closed API raises concerns about transparency, reproducibility, and safety. Further work is needed to evaluate our method with respect to these issues, specifically focusing on explaining explanation faithfulness. Addressing these limitations will be crucial in realizing the full potential of LLMs in conceptual engineering.
Conclusion
In conclusion, this article demonstrates the potential of LLMs in conceptual engineering, focusing on their impact on society and success conditions for CE. By using classification procedures to evaluate and improve the alignment between natural language definitions of concepts and their representation in knowledge graphs, we can enhance the accuracy and relevance of search results and recommendations. While there are limitations to consider, this approach provides a new perspective on success conditions for CE, leading to ameliorative refinement of knowledge graphs as a topic for future research.
