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#text-to-sql

3 curated events
papersTODAY 04:00 UTC

Paper Proposes LLM Routing to Optimize Text-to-SQL Generation

A new arXiv preprint describes a method that routes text-to-SQL requests between different large language models rather than relying on a single model. The approach aims to keep accuracy on complex database queries while cutting the latency and cost that come from always using the most capable model. The authors frame the work as a way to balance performance against efficiency in natural-language database access.

text-to-sql/llm-routing/arxiv/efficiency
papersSEP 10 04:00 UTC

DexterSQL Paper Proposes Deep Schema Exploration and Rule-Based Correction for Text-to-SQL

A new arXiv paper, cross-listed in cs.CL and cs.AI, introduces DexterSQL, a prompting-based approach to text-to-SQL generation that avoids fine-tuning the underlying large language model. The method targets shortcomings of existing prompting techniques, such as relying on coarse-grained schema information, by exploring database schemas in greater depth and applying rule-based corrections to the generated queries.

text-to-sql/llm/databases/research
papersSEP 12 04:00 UTC

TRUST-SQL Uses Multi-Turn Reinforcement Learning for Text-to-SQL on Unknown Schemas

A new arXiv paper introduces TRUST-SQL, a tool-integrated multi-turn reinforcement learning approach for text-to-SQL tasks where the database schema is not known in advance. The authors argue that the common Full Schema Assumption breaks down in enterprise settings, where databases can hold hundreds of tables and noisy metadata. Their method instead lets the model interact with external tools across multiple turns to discover and query the schema.

text-to-sql/reinforcement-learning/agents/arxiv