Database Paper Browser

Back to papers

Revisiting Prompt Engineering via Declarative Crowdsourcing

Summary: Treats LLMs as crowd workers and introduces declarative prompt engineering: applying declarative crowdsourcing concepts—multiple prompting strategies, consistency checks, and hybrid LLM/non‑LLM pipelines—to make prompt design systematic and cost-aware. Validated on sorting, entity resolution, and imputation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
527
Venue
CIDR
Year
2024
Pagerank
6.7106924e-05
Overall Rank
3,840 | 73.29%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
94 CrowdDB: Answering Queries with Crowdsourcing 2011 SIGMOD 0.00051013264
249 Crowdsourced Databases: Query Processing with People 2011 CIDR 0.00030740523
263 CrowdER: Crowdsourcing Entity Resolution 2012 VLDB 0.00029862413
267 Human-powered Sorts and Joins 2012 VLDB 0.00029690405
319 Evaluation of entity resolution approaches on real-world match problems 2010 VLDB 0.00027781866
517 Can Foundation Models Wrangle Your Data? 2023 VLDB 0.00021169035
859 So Who Won? Dynamic Max Discovery with the Crowd 2012 SIGMOD 0.00015870894
866 Leveraging Transitive Relations for Crowdsourced Joins 2013 SIGMOD 0.00015801196
1,164 CrowdScreen: Algorithms for Filtering Data with Humans 2012 SIGMOD 0.00013564823
2,334 Counting with the Crowd 2013 VLDB 9.0161817e-05
Previous Page 1 / 1 Next

Semantically Similar Papers