4 citations · 15 across the 13 of their papers we have counts for
13 papers
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
Jalal Mahmud, Eser Kandogan
Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workf…
Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications
Moin Aminnaseri, Farima Fatahi Bayat, Nikita Bhutani +17
NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users expre…
Effectiveness of Prompt Optimization in NL2SQL Systems
Sairam Gurajada, Eser Kandogan, Sajjadur Rahman
NL2SQL approaches have greatly benefited from the impressive capabilities of large language models (LLMs). In particular, bootstrapping an NL2SQL system for a specific domain can b…
Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI
Eser Kandogan, Nikita Bhutani, Dan Zhang +3
Large language models (LLMs) have gained significant interest in industry due to their impressive capabilities across a wide range of tasks. However, the widespread adoption of LLM…
Towards Operationalizing Heterogeneous Data Discovery
Jin Wang, Yanlin Feng, Chen Shen +2
Querying and exploring massive collections of data sources, such as data lakes, has been an essential research topic in the database community. Although many efforts have been paid…
MageSQL: Enhancing In-context Learning for Text-to-SQL Applications with Large Language Models
Chen Shen, Jin Wang, Sajjadur Rahman +1
The text-to-SQL problem aims to translate natural language questions into SQL statements to ease the interaction between database systems and end users. Recently, Large Language Mo…
A Blueprint Architecture of Compound AI Systems for Enterprise
Eser Kandogan, Sajjadur Rahman, Nikita Bhutani +11
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases. Towards this goa…
CMDBench: A Benchmark for Coarse-to-fine Multimodal Data Discovery in Compound AI Systems
Yanlin Feng, Sajjadur Rahman, Aaron Feng +2
Compound AI systems (CASs) that employ LLMs as agents to accomplish knowledge-intensive tasks via interactions with tools and data retrievers have garnered significant interest wit…
Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions
Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani +3
Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards…
MEGAnno: Exploratory Labeling for NLP in Computational Notebooks
Dan Zhang, Hannah Kim, Rafael Li Chen +2
We present MEGAnno, a novel exploratory annotation framework designed for NLP researchers and practitioners. Unlike existing labeling tools that focus on data labeling only, our fr…
Machop: an End-to-End Generalized Entity Matching Framework
Jin Wang, Yuliang Li, Wataru Hirota +1
Real-world applications frequently seek to solve a general form of the Entity Matching (EM) problem to find associated entities. Such scenarios include matching jobs to candidates…
IBM Functional Genomics Platform, A Cloud-Based Platform for Studying Microbial Life at Scale
Edward E. Seabolt, Gowri Nayar, Harsha Krishnareddy +7
The rapid growth in biological sequence data is revolutionizing our understanding of genotypic diversity and challenging conventional approaches to informatics. With the increasing…
HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop
Yiwei Yang, Eser Kandogan, Yunyao Li +2
While the role of humans is increasingly recognized in machine learning community, representation of and interaction with models in current human-in-the-loop machine learning (HITL…