aiqbench analysis
This paper proposes an offline method for improving LLM text-to-SQL generation on streaming SQL dialects (Confluent Cloud Flink SQL) by iteratively refining a frozen prompt rather than fine-tuning weights or adding retrieval. The reported gains of 7.1 to 19.2 percentage points across seven generators come from a controlled synthetic workload with 30 seed examples, so transfer to production query distributions is untested. For practitioners, the useful takeaway is that dialect-specific prompt engineering, automated with judge feedback and a regression guard, may be a low-cost alternative to retrieval or fine-tuning for niche SQL dialects.
Self-Refining Prompt Induction for Streaming Text-to-SQL
arXiv:2611.00960v1 Announce Type: cross Abstract: Text-to-SQL assistants for streaming engines must handle dialect-specific types, windowing rules, and temporal semantics, including constructs poorly handled by generic prompts. We present Self-Refining Prompt Induction (SRPI), an offline method that converts generation failures into reusable dialect instructions while keeping model weights fixed. A Refiner revises the Generator's prompt using reference-conditioned feedback from three LLM judges; a sampled regression guard checks earlier successes, and a separate Compressor reduces prompt length. Inference uses the frozen prompt without retrieval or query repair. We evaluate cross-domain and cross-model transfer on Confluent Cloud Flink SQL using a controlled synthetic workload seeded by 30 expert-written examples, with 335 induction examples and 257 questions from held-out domains. The same induced prompt improves mean judge scores across seven Generators by 7.1-19.2 percentage points, including an increase from 85.3% to 97.5% for the induction model. A separate compression study reduces prompt length by 52.7% with a 0.19-percentage-point score decrease. An assistant using the induced prompt is in early access, with initial user feedback to inform subsequent refinement.
Abstract:Text-to-SQL assistants for streaming engines must handle dialect-specific types, windowing rules, and temporal semantics, including constructs poorly handled by generic prompts. We present Self-Refining Prompt Induction (SRPI), an offline method that converts generation failures into reusable dialect instructions while keeping model weights fixed. A Refiner revises the Generator's prompt using reference-conditioned feedback from three LLM judges; a sampled regression guard checks earlier successes, and a separate Compressor reduces prompt length. Inference uses the frozen prompt without retrieval or query repair. We evaluate cross-domain and cross-model transfer on Confluent Cloud Flink SQL using a controlled synthetic workload seeded by 30 expert-written examples, with 335 induction examples and 257 questions from held-out domains. The same induced prompt improves mean judge scores across seven Generators by 7.1-19.2 percentage points, including an increase from 85.3% to 97.5% for the induction model. A separate compression study reduces prompt length by 52.7% with a 0.19-percentage-point score decrease. An assistant using the induced prompt is in early access, with initial user feedback to inform subsequent refinement.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB)
arXiv:2611.00960 [cs.CL]
(or arXiv:2611.00960v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.00960 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 269 words