Parrish, Alicia

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

PaLM 2 Technical Report

Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 834

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

BBQ: A Hand-Built Bias Benchmark for Question Answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. R. (2021) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 189

BBQ: A hand-built bias benchmark for question answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. (2022) · Findings of the Association for Computational Linguistics: ACL 2022

Ethics and Social Impacts of AITopic Modeling

Cited by 186

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 149

A toolbox for surfacing health equity harms and biases in large language models

Pfohl, S. R., Cole-Lewis, H., Sayres, R., Neal, D., Asiedu, M., Dieng, A., Tomasev, N., Rashid, Q. M., Azizi, S., Rostamzadeh, N., et al. (2024) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 113

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

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 51

Inverse Scaling: When Bigger Isn't Better

McKenzie, I. R., Lyzhov, A., Pieler, M., Parrish, A., Mueller, A., Prabhu, A., McLean, E., Kirtland, A., Ross, A., Liu, A., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

BLiMP: the benchmark of linguistic minimal pairs for English

Warstadt, A., Parrish, A., Liu, H., Mohananey, A., Peng, W., Wang, S.-F., & Bowman, S. R. (2020) · Transactions of the Association for Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 28

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2021) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 10

Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

Chen, A., Phang, J., Parrish, A., Padmakumar, V., Zhao, C., Bowman, S. R., & Cho, K. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Vidgen, B., Agrawal, A., Ahmed, A. M., Akinwande, V., Al-Nuaimi, N., Alfaraj, N., Alhajjar, E., Aroyo, L., Bavalatti, T., Bartolo, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 5

DataPerf: Benchmarks for Data-Centric AI Development

Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W. G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H. R., et al. (2023) · Advances in Neural Information Processing Systems 36

Advanced Database Systems and QueriesScientific Computing and Data Management

Cited by 5