Krishna, Kalpesh

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

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 407

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · arXiv

Authorship Attribution and ProfilingSpam and Phishing Detection

Cited by 150

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · Advances in Neural Information Processing Systems 36

Authorship Attribution and ProfilingSpam and Phishing Detection

Cited by 150

Hurdles to Progress in Long-form Question Answering

Krishna, K., Roy, A., & Iyyer, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingInformation Retrieval and Search Behavior

Cited by 114

Thieves on Sesame Street! Model Extraction of BERT-based APIs

Krishna, K., Tomar, G. S., Parikh, A. P., Papernot, N., & Iyyer, M. (2019) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 111

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

Generating Question-Answer Hierarchies

Krishna, K., & Iyyer, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 44

Do Long-Range Language Models Actually Use Long-Range Context?

Sun, S., Krishna, K., Mattarella-Micke, A., & Iyyer, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 43

RankGen: Improving Text Generation with Large Ranking Models

Krishna, K., Chang, Y., Wieting, J., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 30

LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization

Krishna, K., Bransom, E., Kuehl, B., Iyyer, M., Dasigi, P., Cohan, A., & Lo, K. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Topic ModelingAdvanced Text Analysis Techniques

Cited by 29

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization

Krishna, K., Bransom, E., Kuehl, B., Iyyer, M., Dasigi, P., Cohan, A., & Lo, K. (2023) · arXiv

Topic ModelingAdvanced Text Analysis Techniques

Cited by 3

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