Zettlemoyer, Luke

Do Membership Inference Attacks Work on Large Language Models?

Duan, M., Suri, A., Mireshghallah, N., Min, S., Shi, W., Zettlemoyer, L., Tsvetkov, Y., Choi, Y., Evans, D., & Hajishirzi, H. (2024) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 17

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

s1: Simple test-time scaling

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., & Hashimoto, T. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12

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

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

Scaling Laws for Generative Mixed-Modal Language Models

Aghajanyan, A., Yu, L., Conneau, A., Hsu, W.-N., Hambardzumyan, K., Zhang, S., Roller, S., Goyal, N., Levy, O., & Zettlemoyer, L. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 10

Scaling Retrieval-Based Language Models with a Trillion-Token Datastore

Shao, R., He, J., Asai, A., Shi, W., Dettmers, T., Min, S., Zettlemoyer, L., & Koh, P. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingInformation Retrieval and Search Behavior

Cited by 8

OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs

Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D'arcy, M., et al. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 7

Trusting Your Evidence: Hallucinate Less with Context-aware Decoding

Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., & Yih, S. W.-T. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

Shepherd: A Critic for Language Model Generation

Wang, T., Yu, P., Tan, X. E., O'Brien, S., Pasunuru, R., Dwivedi-Yu, J., Golovneva, O., Zettlemoyer, L., Fazel-Zarandi, M., & Celikyilmaz, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5

Comparing Hallucination Detection Metrics for Multilingual Generation

Kang, H., Blevins, T., & Zettlemoyer, L. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

2 OLMo 2 Furious

OLMo, T., Walsh, P., Soldaini, L., Groeneveld, D., Lo, K., Arora, S., Bhagia, A., Gu, Y., Huang, S., Jordan, M., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

Spurious Rewards: Rethinking Training Signals in RLVR

Shao, R., Li, S. S., Xin, R., Geng, S., Wang, Y., Oh, S., Du, S. S., Lambert, N., Min, S., Krishna, R., et al. (2025) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 1

Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models

Nguyen, T., Li, Y., Golovneva, O., Zettlemoyer, L., Oh, S., Schmidt, L., & Li, X. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

JobBench: Aligning Agent Work With Human Will

Li, Y., Feng, Y., Xu, Z., Ma, Z., Zheng, K., Jiang, F., Sun, X., Shao, R., Chen, Z., Huang, Y., et al. (2026) · arXiv

Ethics and Social Impacts of AIAI and HR Technologies

Cited by 0