Yasunaga, Michihiro

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 1,034

Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 869

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

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Data Classification

Cited by 518

Holistic Evaluation of Language Models

Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 255

REPLUG: Retrieval-Augmented Black-Box Language Models

Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 209

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 193

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

Gutiérrez, B. J., Shu, Y., Gu, Y., Yasunaga, M., & Su, Y. (2024) · arXiv

Topic ModelingMemory and Neural Mechanisms

Cited by 152

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

Yasunaga, M., & Liang, P. (2020) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 81

VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering

Wang, Y., Yasunaga, M., Ren, H., Wada, S., & Leskovec, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsAdvanced Graph Neural Networks

Cited by 68

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

Overview and Results: CL-SciSumm Shared Task 2019

Chandrasekaran, M. K., Yasunaga, M., Radev, D., Freitag, D., & Kan, M.-Y. (2019) · arXiv

Advanced Text Analysis TechniquesTopic Modeling

Cited by 27