Papers by Daniil Larionov
PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics (2025.naacl-long)
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| Challenge: | Recent efforts to improve the quality of machine-generated natural language content have been limited due to the large token usage required by complex evaluation prompts. |
| Approach: | They propose a prompt optimization approach that uses a smaller, fine-tuned language model to compress input data for evaluation prompt, thus reducing token usage and computational cost when using larger LLMs for downstream evaluation. |
| Outcome: | The proposed approach reduces token usage and costs by 2.37 compared with larger LLMs for downstream evaluation. |
EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics (2023.findings-emnlp)
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| Challenge: | a recent surge of interest in developing evaluation metrics based on pretrained large language models (LLMs) can better cope with lexical variation. |
| Approach: | They propose to replace computation-intensive transformers with lighter alternatives and employ linear and quadratic approximations for alignment algorithms on top of LLM representations. |
| Outcome: | The proposed approach replaces computation-intensive transformers with lighter alternatives and employs linear and quadratic approximations for alignment algorithms on top of LLM representations. |
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (2021.eacl-main)
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Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko
| Challenge: | Annotating training data for sequence tagging of texts is usually very time-consuming . active learning can help to reduce the amount of annotation required to train a good model by multiple times . |
| Approach: | They are the first to thoroughly investigate active learning and transfer learning for natural language processing . they propose to combine active learning with active learning to improve model acquisition . |
| Outcome: | The proposed combination of active learning and Bayesian uncertainty estimation improves performance and reduces obstacles for applying it in practice. |
ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts (2022.emnlp-demos)
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Akim Tsvigun, Leonid Sanochkin, Daniil Larionov, Gleb Kuzmin, Artem Vazhentsev, Ivan Lazichny, Nikita Khromov, Danil Kireev, Aleksandr Rubashevskii, Olga Shahmatova, Dmitry V. Dylov, Igor Galitskiy, Artem Shelmanov
| Challenge: | Currently, the framework supports text classification, sequence tagging, and seq2seq tasks. |
| Approach: | They propose an open-source framework for active learning annotation in natural language processing that provides an easy-to-deploy GUI annotation tool directly in the Jupyter IDE. |
| Outcome: | The proposed framework reduces computational overhead and duration of AL iterations and increases annotated data reusability. |
xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metrics (2024.emnlp-main)
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| Challenge: | State-of-the-art trainable machine translation evaluation metrics rely on large encoders . this makes them computationally expensive and inaccessible to researchers with limited resources. |
| Approach: | They propose a method to extract knowledge stored in large encoders and a pipeline for efficient black-box distillation. |
| Outcome: | The proposed model surpasses COMET-22 and BLEURT-20 on the WMT22 dataset by 6.4%. |
Towards Computationally Feasible Deep Active Learning (2022.findings-naacl)
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Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov
| Challenge: | Active learning (AL) is a technique for reducing the amount of annotation required for training machine learning models. |
| Approach: | They propose two techniques that reduce the amount of time required for AL . they use pseudo-labeling and distilled models to train a successor model . |
| Outcome: | The proposed algorithm reduces the time and computational overhead required to train an acquisition model and estimate uncertainty on instances in the unlabeled pool. |