1 citations · 1 across the 15 of their papers we have counts for
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ML Engineering Agents - A Data Management Perspective
Arnab Phani, Elias Strauss, Tim Schuster +1
Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development. Using large language models for code generation and pred…
"Will This Data Break My Task?" - Interactive Synthesis of Task-Aware Data Unit Tests
Hao Chen, Arnab Phani, Sebastian Schelter
Data is a central resource for modern enterprises and institutions, and data errors propagating through data pipelines lead to serious impact in production. Therefore, data validat…
Beyond Scale and Generation: Understanding Language Model-based Entity Matching
Zeyu Zhang, Xue Li, Iacer Calixto +2
Entity matching identifies records that refer to the same real-world entity. Language models can be adapted to this task through bi-encoder, cross-encoder, and generative matcher a…
ArtiFact: A Large-Scale Multi-Modal Cultural Heritage Dataset
Luciano Duarte, Olga Ovcharenko, Sebastian Schelter
Multi-modal data management has emerged as a central research topic in the database community, spanning data integration, semantic query processing, and data quality assessment. De…
stratum: A System Infrastructure for Massive Agent-Centric ML Workloads
Arnab Phani, Elias Strauss, Sebastian Schelter
Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated. LLMs enable a new type of workload, agentic pipeline sear…
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework
Chuangtao Ma, Zeyu Zhang, Arijit Khan +2
Large language models (LLMs) offer a compelling approach to entity matching (EM) in label-scarce settings, yet their matching quality is sensitive to records with ambiguous or spar…
AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?
Yuchen Tian, Kaixin Li, Hao Chen +5
Large Language Models (LLMs) have recently demonstrated strong capabilities in translating natural language into database queries, especially when dealing with complex graph-struct…