Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
Personas with Attitudes: Controlling LLM s for Diverse Data Annotation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Cross-lingual transfer and language-specific data efforts are interdependent and complementary for effective low-resource NLP, as demonstrated through Luxembourgish case studies and synthesis.
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.
Fine-tuning LLMs by adapting the mdok approach produces competitive results on binary detection, source attribution, and hybrid/adversarial code identification in SemEval-2026 Task 13.
citing papers explorer
-
The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
-
Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish
Cross-lingual transfer and language-specific data efforts are interdependent and complementary for effective low-resource NLP, as demonstrated through Luxembourgish case studies and synthesis.
-
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.
-
mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
Fine-tuning LLMs by adapting the mdok approach produces competitive results on binary detection, source attribution, and hybrid/adversarial code identification in SemEval-2026 Task 13.