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ORCIDGleb Kuzmin
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2020 – today
- 2024
[c7]Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov:
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. ACL (Findings) 2024: 9367-9385- [i3]Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov:
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. CoRR abs/2403.04696 (2024) - [i2]Gleb Kuzmin, Nemeesh Yadav, Ivan V. Smirnov, Timothy Baldwin, Artem Shelmanov:
Inference-Time Selective Debiasing. CoRR abs/2407.19345 (2024) - 2023
- [c6]Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov:
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks. ACL (1) 2023: 11659-11681 - [c5]Gleb Kuzmin, Artem Vazhentsev, Artem Shelmanov, Xudong Han, Simon Suster, Maxim Panov, Alexander Panchenko, Timothy Baldwin:
Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? IJCNLP (1) 2023: 744-770 - 2022
- [c4]Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov:
Uncertainty Estimation of Transformer Predictions for Misclassification Detection. ACL (1) 2022: 8237-8252 - [c3]Akim Tsvigun, Leonid Sanochkin, Daniil Larionov, Gleb Kuzmin, Artem Vazhentsev, Ivan Lazichny, Nikita Khromov, Danil Kireev, Aleksandr Rubashevskii, Olga Shahmatova:
ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts. EMNLP (Demos) 2022: 406-434 - [c2]Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov:
Towards Computationally Feasible Deep Active Learning. NAACL-HLT (Findings) 2022: 1198-1218 - [i1]Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov:
Towards Computationally Feasible Deep Active Learning. CoRR abs/2205.03598 (2022)
2010 – 2019
- 2017
- [c1]Konstantin Fursov, Gleb Kuzmin:
Multidimensional assessment of R&D performance: evidence from the pilot evaluation exercise of Russian public research institutions. ISSI 2017: 1460-1465
Coauthor Index
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