Most cited papers
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Document-Level Machine Translation with Large Language Models
Wang, L., Lyu, C., Ji, T., Zhang, Z., Yu, D., Shi, S., & Tu, Z. (2023) · arXiv
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RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · arXiv
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Ctrl-World: A Controllable Generative World Model for Robot Manipulation
Guo, Y., Shi, L. X., Chen, J., & Finn, C. (2025) · arXiv
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Entropy generation and momentum transfer in the superconductor-normal and normal-superconductor phase transformations and the consistency of the conventional theory of superconductivity
Hirsch, J. E. (2017) · arXiv
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Epistemic Sybil Resistance: Multiplying AI Agents Without Multiplying Evidence
Bara, M. (2026) · arXiv
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Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks
Kirk, J. R., Wray, R. E., Lindes, P., & Laird, J. E. (2022) · arXiv
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AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Wallach, I., Dzamba, M., & Heifets, A. (2015) · arXiv
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Fault-tolerant quantum computation by anyons
Kitaev, A. Y. (1997) · arXiv
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LAraBench: Benchmarking Arabic AI with Large Language Models
Abdelali, A., Mubarak, H., Chowdhury, S. A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y. E., Izham, D., Dalvi, F., Hawasly, M., et al. (2023) · arXiv
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Generative AI in Game Development: A Qualitative Research Synthesis
Ternar, A., Denisova, A., Cunha, J. M., Kultima, A., & Guckelsberger, C. (2025) · arXiv
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Charles Dickens and the Critique of Victorian Society: Literature as a Catalyst for Social Reform
Padra, D. B. (2024) · The Criterion
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The ICF
Eadie, T. L. (2003) · American Journal of Speech-Language Pathology
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Fairness in Serving Large Language Models
Sheng, Y., Cao, S., Li, D., Zhu, B., Li, Z., Zhuo, D., Gonzalez, J. E., & Stoica, I. (2024) · arXiv
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Towards a Framework for Assessing the Impact of Cadastral Development on Land Rights-Holders
Hull, S., & Whittal, J. (2016) · figshare
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Sampling of Structure and Sequence Space of Small Protein Folds
Linsky, T., Noble, K., Tobin, A., Crow, R., Carter, L., Urbauer, J., Baker, D., & Strauch, E. (2021)
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LabQAR (Lab oratory Q uestion A nswering with R eference Ranges) Data Set
bhasuran, B., & He, Z. (2026) · figshare
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Assessment of Cd, Cr and Pb Pollution in Sediment and Water of Gheshlagh River, Iran, in September 2013
Majnoni, F., Alipour, H., Hassanpour, M., Banagar, G., & Ajorlo, M. (2015) · Unpublished
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Retrieval-Augmented Generation with Conflicting Evidence
Wang, H., Prasad, A., Stengel-Eskin, E., & Bansal, M. (2025) · arXiv
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Replication data for: What Can Machines Learn, and What Does It Mean for Occupations and the Economy?
Brynjolfsson, E., Mitchell, T., & Rock, D. (2018) · ICPSR - Interuniversity Consortium for Political and Social Research
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Fredholm Operators and Spectral Flow
Waterstraat, N. (2016) · arXiv
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Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
Yuan, H., Chan, S., Creagh, A. P., Tong, C., Acquah, A., Clifton, D. A., & Doherty, A. (2022) · arXiv
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Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
Shi, Y., Chi, M., Barnes, T., & Price, T. (2022) · arXiv
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Intercellular signaling dynamics from a single cell atlas of the biomaterials response
Cherry, C., Maestas, D. R., Han, J., Andorko, J. I., Cahan, P., Fertig, E. J., Garmire, L. X., & Elisseeff, J. H. (2020)
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MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
Ye, R., Tang, S., Ge, R., Du, Y., Yin, Z., Chen, S., & Shao, J. (2025) · arXiv
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