Yih, Wen-tau

Dense Passage Retrieval for Open-Domain Question Answering

Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 5,311

Dense passage retrieval for open-domain question answering

Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 3,575

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv

Safety Systems Engineering in AutonomyEthics and Social Impacts of AI

Cited by 3,101

WikiQA: A Challenge Dataset for Open-Domain Question Answering

Yang, Y., Yih, W.-T., & Meek, C. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 829

TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Yin, P., Neubig, G., Yih, W.-T., & Riedel, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 487

Dissecting Contextual Word Embeddings: Architecture and Representation

Peters, M., Neumann, M., Zettlemoyer, L., & Yih, W.-T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 434

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

InCoder: A Generative Model for Code Infilling and Synthesis

Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, W.-T., Zettlemoyer, L., & Lewis, M. (2022) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 187

Unsupervised Question Decomposition for Question Answering

Perez, E., Lewis, P., Yih, W.-T., Cho, K., & Kiela, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 123

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Xiong, W., Li, X. L., Iyer, S., Du, J., Lewis, P., Wang, W. Y., Mehdad, Y., Yih, W.-T., Riedel, S., Kiela, D., et al. (2020) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 112

Improving Passage Retrieval with Zero-Shot Question Generation

Sachan, D., Lewis, M., Joshi, M., Aghajanyan, A., Yih, W.-T., Pineau, J., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Information Retrieval and Search BehaviorTopic Modeling

Cited by 90

REPLUG: Retrieval-augmented black-box language models

Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 65

Memory Layers at Scale

Berges, V.-P., Oğuz, B., Haziza, D., Yih, W.-T., Zettlemoyer, L., & Ghosh, G. (2024) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 46

Task-aware Retrieval with Instructions

Asai, A., Schick, T., Lewis, P., Chen, X., Izacard, G., Riedel, S., Hajishirzi, H., & Yih, W.-T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Information Retrieval and Search BehaviorTopic Modeling

Cited by 46

LEVER: Learning to Verify Language-to-Code Generation with Execution

Ni, A., Iyer, S., Radev, D., Stoyanov, V., Yih, W.-T., Wang, S. I., & Lin, X. V. (2023) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 46

Continual Learning via Sparse Memory Finetuning

Lin, J., Zettlemoyer, L., Ghosh, G., Yih, W.-T., Markosyan, A., Berges, V.-P., & Oğuz, B. (2025) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 33

The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus

Piktus, A., Petroni, F., Karpukhin, V., Okhonko, D., Broscheit, S., Izacard, G., Lewis, P., Oğuz, B., Grave, E., Yih, W.-T., et al. (2021) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 22

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

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

FLAME: Factuality-Aware Alignment for Large Language Models

Lin, S.-C., Gao, L., Oguz, B., Xiong, W., Lin, J., Yih, W.-T., & Chen, X. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

Instruction-tuned Language Models are Better Knowledge Learners

Jiang, Z., Sun, Z., Shi, W., Rodriguez, P., Zhou, C., Neubig, G., Lin, X. V., Yih, W.-T., & Iyer, S. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 3