Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
LLM-generated ML pipelines show higher bias (87.7% sensitive attributes) than conditional statements (59.2%), indicating that simple if-statement tests underestimate bias risk in practical code generation.
citing papers explorer
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Cross-Lingual Exploration for Parametric Knowledge
Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
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From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation
LLM-generated ML pipelines show higher bias (87.7% sensitive attributes) than conditional statements (59.2%), indicating that simple if-statement tests underestimate bias risk in practical code generation.