Papers by Stephanie Eckman
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance (2023.findings-emnlp)
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| Challenge: | Using an annotation instrument, the design of the annotation instrument and the instructions given to annotators can impact training data. |
| Approach: | They investigate the impact of an annotation instrument on training data . they collect hate speech and offensive language annotations in a tweet corpus . |
| Outcome: | The proposed model performs better on holdout conditions than on the standard model. |
Mitigating Selection Bias with Node Pruning and Auxiliary Options (2025.acl-long)
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| Challenge: | Large language models exhibit systematic preferences for answer choices when answering multiple-choice questions. |
| Approach: | They propose two methods to identify and remove internal sources of selection bias . they propose Choice Kullback-Leibler Divergence (CKLD) to capture distributional imbalances in model predictions. |
| Outcome: | The proposed methods improve answer accuracy while reducing selection bias. |
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs (2026.acl-short)
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| Challenge: | Using long-term memory, large language models can embed social hierarchies into their emotional reasoning. |
| Approach: | They evaluate 15 large language models on validated emotional intelligence tests to examine how user memory affects emotional intelligence. |
| Outcome: | The results show that the models with advantaged profiles receive more accurate emotional interpretations. |