← Search

Ozlem Uzuner

2 accepted papers

2025

Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions

NAACL 2025findings

Large language models (LLMs) have demonstrated great performance across various benchmarks, showing potential as general-purpose task solvers. However, as LLMs are typically trained on vast amounts of data, a significant concern in their evaluation is data contamination, where overlap between traini…

Cited by 3SourcePDFScholar
2025

Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models

ACL 2025short

Social determinants of health (SDOH) extraction from clinical text is critical for downstream healthcare analytics. Although large language models (LLMs) have shown promise, they may rely on superficial cues leading to spurious predictions. Using the MIMIC portion of the SHAC (Social History Annotat…