Aroyo, Lora

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

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI

Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIData Quality and Management

Cited by 781

LaMDA: Language Models for Dialog Applications

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 778

The effects of transparency on trust in and acceptance of a content-based art recommender

Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., & Wielinga, B. (2008) · User Modeling and User-Adapted Interaction

Recommender Systems and TechniquesExplainable Artificial Intelligence (XAI)

Cited by 448

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

Measuring Attribution in Natural Language Generation Models

Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2023) · Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 71

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

Crowdsourcing Ground Truth for Medical Relation Extraction

Dumitrache, A., Aroyo, L., & Welty, C. (2018) · ACM Transactions on Interactive Intelligent Systems

Biomedical Text Mining and OntologiesMobile Crowdsensing and Crowdsourcing

Cited by 60

CrowdTruth: Machine-Human Computation Framework for Harnessing Disagreement in Gathering Annotated Data

Inel, O., Khamkham, K., Cristea, T., Dumitrache, A., Rutjes, A., van der Ploeg, J., Romaszko, L., Aroyo, L., & Sips, R.-J. (2014) · Lecture Notes in Computer Science

Mobile Crowdsensing and CrowdsourcingComputational and Text Analysis Methods

Cited by 57

The Three Sides of CrowdTruth

Aroyo, L., & Welty, C. (2014) · Human Computation

Mobile Crowdsensing and CrowdsourcingSpam and Phishing Detection

Cited by 43

Measuring Attribution in Natural Language Generation Models

Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 34

Data excellence for AI: why should you care?

Aroyo, L., Lease, M., Paritosh, P., & Schaekermann, M. (2022) · Interactions

Innovative Human-Technology InteractionEthics and Social Impacts of AI

Cited by 31

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