9 citations · 25 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models
Parsa Kavehzadeh, Mohammadreza Pourreza, Mojtaba Valipour +5
Deployment of autoregressive large language models (LLMs) is costly, and as these models increase in size, the associated costs will become even more considerable. Consequently, di…
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning
Yuxi Feng, Raymond Li, Zhenan Fan +4
While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in transla…
SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder
Mohammadreza Pourreza, Davood Rafiei, Yuxi Feng +3
Detecting structural similarity between queries is essential for selecting examples in in-context learning models. However, assessing structural similarity based solely on the natu…
DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models
Mohammadreza Pourreza, Davood Rafiei
Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy. Closing the performance gap between small open-…
Evaluating Cross-Domain Text-to-SQL Models and Benchmarks
Mohammadreza Pourreza, Davood Rafiei
Text-to-SQL benchmarks play a crucial role in evaluating the progress made in the field and the ranking of different models. However, accurately matching a model-generated SQL quer…