Papers by Ozlem Uzuner
Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large language models have demonstrated great performance across various benchmarks, but data contamination is a concern in their evaluation. |
| Approach: | They analyze 50 papers on data contamination detection and test three of them as case studies to identify the possibility of data contamination. |
| Outcome: | The proposed methods can detect membership inference attacks on instance-level data, and can perform similar to random guessing on LLM pretraining datasets. |
Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models (2025.acl-short)
Copied to clipboard
| Challenge: | Large language models (LLMs) rely on superficial cues leading to spurious predictions . recent work has highlighted how LLMs exploit spurious patterns rather than learning causal, generalizable features. |
| Approach: | They use a social history annotation corpus dataset to examine drug status extraction . they evaluate prompt engineering and chain-of-thought reasoning to reduce false positives . |
| Outcome: | The proposed model can predict drug use when alcohol or smoking is not present, while uncovering gender disparities in model performance. |