Lyu, Xinxi

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 755

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 752

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

Dolma: an open corpus of three trillion tokens for language model pretraining research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 56

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

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10