arXiv AI
Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 18
ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models
arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.
arXiv AI
Jun 11
TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
arXiv:2606. 12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult.
arXiv AI
Jun 16
Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning
arXiv:2606. 15598v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively.
arXiv AI
Sep 15
Towards Optimizing SQL Generation via LLM Routing
The paper "Towards Optimizing SQL Generation via LLM Routing" proposes a routing approach for Text-to-SQL tasks that dynamically selects the most cost‑effective large language model (LLM) for each query. Two routing strategies—score‑based and classification‑based—are introduced, achieving accuracy comparable to the best LLM while reducing latency and monetary cost. The authors design the routers for easy training and efficient inference, and demonstrate a practical accuracy‑cost trade‑off on the BIRD dataset.
arXiv AI
Sep 23
LIMIT: Less Is More for Instruction Tuning in Text-to-SQL
LIMIT (Less Is More for Instruction Tuning in Text-to-SQL) challenges the belief that large instruction corpora are necessary for effective Text-to-SQL models. The framework uses a four‑stage data‑centric process—difficulty‑aware filtering, chain‑of‑thought synthesis, LLM‑as‑judge quality scoring, and genetic algorithm optimization—to select a compact set of examples that still achieve full schema coverage. On the BIRD and Spider benchmarks, LIMIT’s 796 and 863 samples enable Qwen3‑8B to reach 69.1% and 88.9% execution accuracy, outperforming methods trained on twenty times more data and setting a new state‑of‑the‑art for open‑source approaches.
arXiv AI
Aug 5
Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks
arXiv:2608. 03794v1 Announce Type: cross Abstract: Large Language Models (LLMs) are transforming database interaction paradigms, evolving from simple query translators to autonomous database administrators (DBAs).