Stephanie Eckman

SATA-Bench : Select All That Apply Benchmark For Multiple Choice Questions

Weijie Xu, shixian cui, Xi Fang, Chi Xue et autres

us (code pays fourni par la source)

DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models

Xi Fang, Weijie Xu, Yingqiang Ge, Yuhui Xu et autres

Personalization changes what a model says to a user; we show that it can also change the reasoning trajectory used to justify the response. Modern LLMs personalize interactions by storing user attributes, preferences, and prior context, then injecting this information into future …

DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models

Xi Fang, Weijie Xu, Yingqiang Ge, Yuhui Xu et autres

Personalization changes what a model says to a user; we show that it can also change the reasoning trajectory used to justify the response. Modern LLMs personalize interactions by storing user attributes, preferences, and prior context, then injecting this information into future …

The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs

Association for Computational Linguistics 2026, Stephanie Eckman, Xi Fang, Chandan K. Reddy et autres

When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional …

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From Ground Truth to Measurement: A Statistical Framework for Human Labeling

Robert Chew, Stephanie Eckman, Christoph Kern, Frauke Kreuter

Supervised machine learning assumes that labeled data provide accurate measurements of the concepts models are meant to learn. Yet in practice, human labeling introduces systematic variation arising from ambiguous items, divergent interpretations, and simple mistakes. Machine learning research commonly treats all disagreement …

From Ground Truth to Measurement: A Statistical Framework for Human Labeling

Robert Chew, Stephanie Eckman, Christoph Kern, Frauke Kreuter

Supervised machine learning assumes that labeled data provide accurate measurements of the concepts models are meant to learn. Yet in practice, human labeling introduces systematic variation arising from ambiguous items, divergent interpretations, and simple mistakes. Machine learning research commonly treats all disagreement …

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Bias in the Loop: How Humans Evaluate AI-Generated Suggestions

Jacob Beck, Stephanie Eckman, Christoph Kern, Frauke Kreuter

Human-AI collaboration increasingly drives decision-making across industries. While AI systems promise efficiency gains by providing automated suggestions for human review, these workflows can trigger cognitive biases that degrade performance. This paper reveals the psychological factors that determine when these collaborations succeed or …

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The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs

Xi Fang, Weijie Xu, Yuchong Zhang, Scott Nickleach et autres

Xi Fang, Weijie Xu, Yuchong Zhang, Scott Nickleach, Stephanie Eckman, Chandan K. Reddy. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2026.

Data of worldwide observational studies of adults with accelerometry-measured physical activity and sedentary behavior

Elissa Scherer, Kelly R. Evenson, Carmen C. Cuthbertson, Stephanie Eckman et autres

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Tropical Data: Approach and Methodology as Applied to Trachoma Prevalence Surveys.

Akoi Zoumanigui, Beido Nassirou, Rob Henry, Scott D. Nash et autres

Population-based prevalence surveys are essential for decision-making on interventions to achieve trachoma elimination as a public health problem. This paper outlines the methodologies of Tropical Data, which supports work to undertake those surveys.Tropical Data is a consortium of partners that supports health …

us (code pays fourni par la source)

Bias in the Loop: How Humans Evaluate AI-Generated Suggestions

Jacob Beck, Stephanie Eckman, Christoph Kern, Frauke Kreuter

Human-AI collaboration increasingly drives decision-making across industries, from medical diagnosis to content moderation. While AI systems promise efficiency gains by providing automated suggestions for human review, these workflows can trigger cognitive biases that degrade performance. We know little about the psychological factors …

SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions

Weijie Xu, Xi Fang, Xue Chi, Stephanie Eckman et autres

Large language models (LLMs) are increasingly evaluated on single-answer multiple-choice tasks, yet many real-world problems require identifying all correct answers from a set of options. This capability remains underexplored. We introduce SATA-BENCH, the first dedicated benchmark for evaluating LLMs on Select All …