Ibrahim, Lujain

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

Bean, A. M., Kearns, R. O., Romanou, A., Hafner, F. S., Mayne, H., Batzner, J., Foroutan Eghlidi, N., Schmitz, C., Korgul, K., Batra, H., et al. (2025) · Advances in Neural Information Processing Systems 38

Topic ModelingNatural Language Processing Techniques

Cited by 110

ELEPHANT: Measuring and understanding social sycophancy in LLMs

Cheng, M., Yu, S., Lee, C., Khadpe, P., Ibrahim, L., & Jurafsky, D. (2025) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 12

Towards interactive evaluations for interaction harms in human-AI systems

Ibrahim, L., Huang, S., Bhatt, U., Ahmad, L., & Anderljung, M. (2024) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 11

Towards Interactive Evaluations for Interaction Harms in Human-AI Systems

Ibrahim, L., Huang, S., Ahmad, L., Bhatt, U., & Anderljung, M. (2025) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 11

Thinking beyond the anthropomorphic paradigm benefits LLM research

Ibrahim, L., & Cheng, M. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

Training language models to be warm and empathetic makes them less reliable and more sycophantic

Ibrahim, L., Hafner, F. S., & Rocher, L. (2025) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 3

Do Explanations Improve the Quality of AI-assisted Human Decisions? An Algorithm-in-the-Loop Analysis of Factual & Counterfactual Explanations

Ibrahim, L., Ghassemi, M. M., & Alhanai, T. (2023) · International Joint Conference on Autonomous Agents and Multiagent Systems

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 1