70 citations · 71 across the 3 of their papers we have counts for
3 papers
cs.LG2024
SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees
Saehan Jo, Immanuel Trummer
The advancement of Large Language Models (LLMs) has significantly boosted performance in natural language processing (NLP) tasks. However, the deployment of high-performance LLMs i…
cs.DB2019★ 70 cited
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning
Immanuel Trummer, Junxiong Wang, Deepak Maram +3
SkinnerDB is designed from the ground up for reliable join ordering. It maintains no data statistics and uses no cost or cardinality models. Instead, it uses reinforcement learning…
cs.DB2018★ 1 cited
Verifying Text Summaries of Relational Data Sets
Saehan Jo, Immanuel Trummer, Weicheng Yu +4
We present a novel natural language query interface, the AggChecker, aimed at text summaries of relational data sets. The tool focuses on natural language claims that translate int…