Webson, Albert
Google (United States)
16 papers on CoChat
Most cited papers
- Verified
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 1,441
- Verified
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
- Verified
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 612
- Verified
Towards conversational diagnostic artificial intelligence
Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., et al. (2025) · Nature
Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare
Cited by 366
- Verified
Crosslingual Generalization through Multitask Finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 361
- Verified
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
- Verified
Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models
Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H., & Rush, A. M. (2022) · arXiv
Topic ModelingData Visualization and Analytics
Cited by 204
- Verified
Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models
Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H., & Rush, A. M. (2022) · IEEE Transactions on Visualization and Computer Graphics
Topic ModelingData Visualization and Analytics
Cited by 204
- Verified
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?
Webson, A., & Pavlick, E. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 203
- Verified
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Bach, S., Sanh, V., Yong, Z. X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Natural Language Processing TechniquesTopic Modeling
Cited by 178
- Verified
Capabilities of Gemini Models in Medicine
Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv
Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications
Cited by 117
- Verified
Larger language models do in-context learning differently
Wei, J., Wei, J., Tay, Y., Tran, D., Webson, A., Lu, Y., Chen, X., Liu, H., Huang, D., Zhou, D., et al. (2023) · arXiv
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 109
- Verified
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
- Verified
Crosslingual Generalization through Multitask Finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Scao, T. L., Bari, M. S., Shen, S., Yong, Z.-X., Schoelkopf, H., et al. (2022) · arXiv
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 29
- Verified
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 21
- Verified
Evaluating Frontier Models for Dangerous Capabilities
Phuong, M., Aitchison, M., Catt, E., Cogan, S., Kaskasoli, A., Krakovna, V., Lindner, D., Rahtz, M., Assael, Y., Hodkinson, S., et al. (2024) · arXiv
Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning
Cited by 11
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