Paper

EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL

Unreadunread

AI summary

arXiv:2606. 23693v1 Announce Type: new Abstract: Text-to-SQL enables users to query databases using natural language by generating executable SQL queries.

Reading status

Priority

About this paper

Authors: Jaehoon Lee, CheolWon Na, Suyoung Bae, Jin-Seop Lee, Jihyung Lee, YunSeok Choi, Jee-Hyong Lee
Released: 2026-06-24 (15d ago)
Topic: Related to current project
Relation to my work: Read next because EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, rect, correct, rate, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.23693

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.