Papers by Ekaterina Taktasheva
A Family of Pretrained Transformer Language Models for Russian (2024.lrec-main)
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Dmitry Zmitrovich, Aleksandr Abramov, Andrey Kalmykov, Vitaly Kadulin, Maria Tikhonova, Ekaterina Taktasheva, Danil Astafurov, Mark Baushenko, Artem Snegirev, Tatiana Shavrina, Sergei S. Markov, Vladislav Mikhailov, Alena Fenogenova
| Challenge: | Developing Transformer language models for the Russian language has received little attention . most of these LMs are developed for English, which imposes substantial constraints on the potential of the language technologies. |
| Approach: | They propose to release 13 Russian Transformer language models that span three languages . they aim to broaden the scope of NLP research directions and develop industrial solutions for the Russian language. |
| Outcome: | The proposed models are based on Russian language datasets and benchmarks. |
TAPE: Assessing Few-shot Russian Language Understanding (2022.findings-emnlp)
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Ekaterina Taktasheva, Tatiana Shavrina, Alena Fenogenova, Denis Shevelev, Nadezhda Katricheva, Maria Tikhonova, Albina Akhmetgareeva, Oleg Zinkevich, Anastasiia Bashmakova, Svetlana Iordanskaia, Alena Spiridonova, Valentina Kurenshchikova, Ekaterina Artemova, Vladislav Mikhailov
| Challenge: | Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes, but lacks standardized evaluation suites for non-English languages. |
| Approach: | They propose a novel benchmark that includes six more complex NLU tasks for Russian, covering multi-hop reasoning, ethical concepts, logic and commonsense knowledge. |
| Outcome: | The proposed benchmark includes six more complex NLU tasks for Russian, covering multi-hop reasoning, ethical concepts, logic and commonsense knowledge. |
TAPS: Tool-Augmented Personalisation via Structured Tagging (2025.emnlp-main)
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| Challenge: | Existing approaches to personalise tool use overlook the role of personalisation in guiding tool use. |
| Approach: | They propose a tool-augmented large language model that integrates user preferences into goal-oriented dialogue agents by leveraging a structured tagging tool and an uncertainty-based tool detector. |
| Outcome: | The proposed solution significantly improves the ability of LLMs to incorporate user preferences, achieving the new state-of-the-art for open source models on the NLSI task. |
RuBLiMP: Russian Benchmark of Linguistic Minimal Pairs (2024.emnlp-main)
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Ekaterina Taktasheva, Maxim Bazhukov, Kirill Koncha, Alena Fenogenova, Ekaterina Artemova, Vladislav Mikhailov
| Challenge: | Existing resources for minimal pairs address a limited number of languages and lack diversity of language-specific grammatical phenomena. |
| Approach: | They propose to use a Russian benchmark of linguistic minimal pairs to evaluate grammatical knowledge of language models. |
| Outcome: | The proposed benchmark includes 45k pairs of sentences that differ in grammaticality and isolate a morphological, syntactic, or semantic phenomenon. |