Schaeffer, Rylan

Are Emergent Abilities of Large Language Models a Mirage?

Schaeffer, R., Miranda, B., & Koyejo, S. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 200

Best-of-N Jailbreaking

Hughes, J., Price, S., Lynch, A., Schaeffer, R., Barez, F., Koyejo, S., Sleight, H., Jones, E., Perez, E., & Sharma, M. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 129

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., et al. (2023) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 110

DecodingTrust: A comprehensive assessment of trustworthiness in GPT models

Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., et al. (2023) · Advances in Neural Information Processing Systems 36

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 107

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells

Schaeffer, R., Khona, M., Ma, T., Eyzaguirre, C., Koyejo, S., & Fiete, I. (2023) · Advances in Neural Information Processing Systems 36

Memory and Neural MechanismsSpatial Cognition and Navigation

Cited by 27

Many-shot Jailbreaking

Anil, C., Durmus, E., Panickssery, N., Sharma, M., Benton, J., Kundu, S., Batson, J., Tong, M., Mu, J., Ford, D., et al. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 25

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Gerstgrasser, M., Schaeffer, R., Dey, A., Rafailov, R., Sleight, H., Hughes, J., Korbak, T., Agrawal, R., Pai, D., Gromov, A., et al. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisTopic Modeling

Cited by 15

Collapse or thrive? perils and promises of synthetic data in a self-generating world

Kazdan, J., Schaeffer, R., Dey, A., Gerstgrasser, M., Rafailov, R., Donoho, D. L., & Koyejo, S. (2024) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 4

Quantifying Variance in Evaluation Benchmarks

Madaan, L., Singh, A. K., Schaeffer, R., Poulton, A., Koyejo, S., Stenetorp, P., Narang, S., & Hupkes, D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Schaeffer, R., Schoelkopf, H., Miranda, B., Mukobi, G., Madan, V., Ibrahim, A., Bradley, H., Biderman, S., & Koyejo, S. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2