Kale, Mihir

mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 1,629

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

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

ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models

Xue, L., Barua, A., Constant, N., Al-Rfou, R., Narang, S., Kale, M., Roberts, A., & Raffel, C. (2022) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 330

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 284

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

Natural Language Processing TechniquesTopic Modeling

Cited by 159

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Anuoluwapo, A., Bosselut, A., Chandu, K. R., Clinciu, M., Das, D., Dhole, K. D., et al. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 49

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Botev, A., De, S., Smith, S. L., Fernando, A., Muraru, G.-C., Haroun, R., Berrada, L., Pascanu, R., Sessa, P. G., Dadashi, R., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5

Let's (not) just put things in Context: Test-Time Training for Long-Context LLMs

Bansal, R., Zhang, A., Tiwari, R., Madaan, L., Duvvuri, S. S., Khatri, D., Brandfonbrener, D., Alvarez-Melis, D., Bhargava, P., Kale, M. S., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0