Lo, Kyle
Allen Institute for Artificial Intelligence
39 papers on CoChatORCID 0000-0002-1804-2853
Most cited papers
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SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Topic ModelingMultimodal Machine Learning Applications
Cited by 3,218
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Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 2,103
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S2ORC: The Semantic Scholar Open Research Corpus
Lo, K., Wang, L. L., Neumann, M., Kinney, R., & Weld, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
scientometrics and bibliometrics researchComputational and Text Analysis Methods
Cited by 457
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Fact or Fiction: Verifying Scientific Claims
Wadden, D., Lin, S., Lo, K., Wang, L. L., van Zuylen, M., Cohan, A., & Hajishirzi, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Misinformation and Its ImpactsTopic Modeling
Cited by 382
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Construction of the Literature Graph in Semantic Scholar
Ammar, W., Groeneveld, D., Bhagavatula, C., Beltagy, I., Crawford, M., Downey, D., Dunkelberger, J., Elgohary, A., Feldman, S., Ha, V., et al. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
scientometrics and bibliometrics researchTopic Modeling
Cited by 358
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TLDR: Extreme Summarization of Scientific Documents
Cachola, I., Lo, K., Cohan, A., & Weld, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020
Advanced Text Analysis TechniquesTopic Modeling
Cited by 169
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A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Topic ModelingInformation Retrieval and Search Behavior
Cited by 138
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The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Scao, T. L., Von Werra, L., Mou, C., Ponferrada, E. G., Nguyen, H., et al. (2023) · arXiv
Natural Language Processing TechniquesTopic Modeling
Cited by 93
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Data Governance in the Age of Large-Scale Data-Driven Language Technology
Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency
Ethics and Social Impacts of AIDigitalization, Law, and Regulation
Cited by 75
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OLMo: Accelerating the Science of Language Models
Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, 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 70
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Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2024) · arXiv
Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications
Cited by 67
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Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications
Cited by 67
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The Semantic Scholar Open Data Platform
Kinney, R., Anastasiades, C., Authur, R., Beltagy, I., Bragg, J., Buraczynski, A., Cachola, I., Candra, S., Chandrasekhar, Y., Cohan, A., et al. (2023) · arXiv
scientometrics and bibliometrics researchSemantic Web and Ontologies
Cited by 64
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MultiVerS: Improving scientific claim verification with weak supervision and full-document context
Wadden, D., Lo, K., Wang, L., Cohan, A., Beltagy, I., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022
Misinformation and Its ImpactsTopic Modeling
Cited by 60
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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
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SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 41
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Synthesizing scientific literature with retrieval-augmented language models
Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D’Arcy, M., et al. (2026) · Nature
Topic ModelingInformation Retrieval and Search Behavior
Cited by 38
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SciFact-Open: Towards open-domain scientific claim verification
Wadden, D., Lo, K., Kuehl, B., Cohan, A., Beltagy, I., Wang, L. L., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022
Topic ModelingMisinformation and Its Impacts
Cited by 35
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SciFact-Open: Towards open-domain scientific claim verification
Wadden, D., Lo, K., Kuehl, B., Cohan, A., Beltagy, I., Wang, L. L., & Hajishirzi, H. (2022) · arXiv
Topic ModelingMisinformation and Its Impacts
Cited by 35
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LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Krishna, K., Bransom, E., Kuehl, B., Iyyer, M., Dasigi, P., Cohan, A., & Lo, K. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Topic ModelingAdvanced Text Analysis Techniques
Cited by 29
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BooookScore: A systematic exploration of book-length summarization in the era of LLMs
Chang, Y., Lo, K., Goyal, T., & Iyyer, M. (2023) · arXiv
Topic ModelingAdvanced Text Analysis Techniques
Cited by 18
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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
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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
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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
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