RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation

Background

Structured query generation involves converting natural language instructions into database queries, a task often executed using large language models. However, these models can produce non-executable queries due to both syntactic and semantic errors. Syntactic failures result in system-generated error messages that are typically discarded at inference time.

Key Points

The research investigates how to effectively allocate compute resources during query generation by focusing on the two methods: Independent Scaling (IS) and Reflection-Augmented Scaling (RAS). IS performs memoryless resampling, treating each attempt independently. In contrast, RAS conditions new attempts based on prior execution feedback via in-context learning (ICL).

• IS Method: This method does not incorporate any context from previous attempts, simply resampling to generate a new query.

• RAS Method: This approach leverages error messages as actionable feedback through ICL. It uses the information from executed queries to inform subsequent attempts, potentially leading to more efficient and executable results.

Significance

The study demonstrates that structuring inference-time compute around execution errors is more effective than independent sampling in improving query executability. Specifically, RAS reduces the Query Execution Error Rate by 41–50% at n=5 compared to IS’s 32–38%. This outcome suggests a significant improvement in query generation accuracy and efficiency.

• Key Insight: Executable feedback from execution errors can be harnessed through ICL to guide subsequent attempts, making the inference process more targeted and effective.

• Implications: The findings imply that integrating actionable error messages into the inference process can lead to substantial improvements in query generation quality. This has direct implications for applications relying on natural language interfaces with databases.

By structuring compute resources around these errors, RAS provides a more strategic approach to query generation, reducing the reliance on independent resampling and leveraging contextual information to enhance performance.