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researcher

Chen Shen

Temple University

10 papers hereh-index 560 citations14 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author3
  • middle author6

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.DB3
  • cs.AI1
  • cs.HC1
  • cs.IR1
  • cs.LG1
affiliations
  • Temple University
  • Megagon Labs
same name
  • Chen Shen — 17 papers, h 20
  • Chen Shen — 15 papers, h 3
  • Chen Shen — 14 papers, h 23
  • Chen Shen — 13 papers, h 31
  • Chen Shen — 12 papers, h 7
  • Chen Shen — 12 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172026
most citedMageSQL: Enhancing In-context Learning for Text-to-SQL Applications with Large Language Models

2 citations · 4 across the 9 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.HC2025

AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems

Hannah Kim, Kushan Mitra, Chen Shen +2

Large language models (LLMs) are being increasingly used for planning in orchestrated multi-agent systems. However, existing LLM-based approaches often fall short of human expectat…

cs.CL2025

Towards Probabilistic Question Answering Over Tabular Data

Chen Shen, Sajjadur Rahman, Estevam Hruschka

Progress in question answering (QA) over tabular data has enabled reliable factual retrieval from relational tables. However, many real-world questions are probabilistic, requiring…

cs.DB2025

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…

cs.DB2025★ 2 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.