Faruqui, Manaal

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

Improving Vector Space Word Representations Using Multilingual Correlation

Faruqui, M., & Dyer, C. (2014) · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 620

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

ToTTo: A Controlled Table-To-Text Generation Dataset

Parikh, A., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingMultimodal Machine Learning Applications

Cited by 252

ToTTo: A Controlled Table-To-Text Generation Dataset

Parikh, A. P., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 252

Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

Dhingra, B., Faruqui, M., Parikh, A., Chang, M.-W., Das, D., & Cohen, W. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 150

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

TIMEDIAL: Temporal Commonsense Reasoning in Dialog

Qin, L., Gupta, A., Upadhyay, S., He, L., Choi, Y., & Faruqui, M. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 59

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Munkhdalai, T., Faruqui, M., & Gopal, S. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 23

Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering

Gupta, A., Xu, J., Upadhyay, S., Yang, D., & Faruqui, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Topic ModelingNatural Language Processing Techniques

Cited by 22

AutoMix: Automatically Mixing Language Models

Aggarwal, P., Madaan, A., Anand, A., Potharaju, S. P., Mishra, S., Zhou, P., Gupta, A., Rajagopal, D., Kappaganthu, K., Yang, Y., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 19

How FaR Are Large Language Models From Agents with Theory-of-Mind?

Zhou, P., Madaan, A., Potharaju, S. P., Gupta, A., McKee, K. R., Holtzman, A., Pujara, J., Ren, X., Mishra, S., Nematzadeh, A., et al. (2023) · arXiv

Topic ModelingChild and Animal Learning Development

Cited by 6

Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation

Krishna, S., Krishna, K., Mohananey, A., Schwarcz, S., Stambler, A., Upadhyay, S., & Faruqui, M. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 1