Text2Cypher: Data Pruning using Hard Example Selection
This provides a cost-effective solution for developers and researchers working with natural language interactions for graph databases, though it is incremental as it builds on existing Text2Cypher methods.
The paper tackles the problem of high fine-tuning costs for Text2Cypher models by proposing five hard-example selection techniques to prune datasets, resulting in halved training time and costs with minimal performance impact.
Database query languages such as SQL for relational databases and Cypher for graph databases have been widely adopted. Recent advancements in large language models (LLMs) enable natural language interactions with databases through models like Text2SQL and Text2Cypher. Fine-tuning these models typically requires large, diverse datasets containing non-trivial examples. However, as dataset size increases, the cost of fine-tuning also rises. This makes smaller, high-quality datasets essential for reducing costs for the same or better performance. In this paper, we propose five hard-example selection techniques for pruning the Text2Cypher dataset, aiming to preserve or improve performance while reducing resource usage. Our results show that these hard-example selection approaches can halve training time and costs with minimal impact on performance, and demonstrates that hard-example selection provides a cost-effective solution.