8 papers
SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models
Emil Biju, Shayan Talaei, Zhemin Huang +3
Large reasoning models (LRMs) excel at complex reasoning tasks but typically generate lengthy sequential chains-of-thought, resulting in long inference times before arriving at the…
Do LLMs Align with My Task? Evaluating Text-to-SQL via Dataset Alignment
Davood Rafiei, Morgan Lindsay Heisler, Weiwei Zhang +2
Supervised Fine-Tuning (SFT) is an effective method for adapting Large Language Models (LLMs) on downstream tasks. However, variability in training data can hinder a model's abilit…
Confidence Estimation for Text-to-SQL in Large Language Models
Sepideh Entezari Maleki, Mohammadreza Pourreza, Davood Rafiei
Confidence estimation for text-to-SQL aims to assess the reliability of model-generated SQL queries without having access to gold answers. We study this problem in the context of l…
On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows
Souradip Chakraborty, Mohammadreza Pourreza, Ruoxi Sun +8
Agentic AI workflows (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low. A promising solution is inference-t…
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL
Mohammadreza Pourreza, Shayan Talaei, Ruoxi Sun +5
Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query fo…
CHESS: Contextual Harnessing for Efficient SQL Synthesis
Shayan Talaei, Mohammadreza Pourreza, Yu-Chen Chang +2
Translating natural language questions into SQL queries, known as text-to-SQL, is a long-standing research problem. Effective text-to-SQL synthesis can become very challenging due…