Papers by Ivan P. Yamshchikov
From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attribution (2026.acl-long)
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| Challenge: | Currently, subword tokenization is the most common approach for vocabulary building in large models. |
| Approach: | They propose to regularize training and minimize overfitting by using source-attributed BPE . they find that undertrained tokens are prone to producing unused, unusable tokens . |
| Outcome: | The proposed techniques reduce the number of under-trained tokens while maintaining the same inference procedure as with regular BPE. |
Vygotsky Distance: Measure for Benchmark Task Similarity (2024.lrec-main)
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| Challenge: | GLUE, SuperGLUE and RussianSuperGLUE benchmarks are arbitrary sets of tasks that are not generalized. |
| Approach: | They propose a theoretical instrument and an algorithm to calculate similarity between benchmark tasks . they use relative performance of the "students" on a given task to determine similarity . |
| Outcome: | The proposed model reduces the number of evaluation tasks while maintaining high validation quality. |
Teaching Old Tokenizers New Words: Efficient Tokenizer Adaptation for Pretrained Models (2026.findings-eacl)
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| Challenge: | Extending existing vocabulary is a widely used step in adapting pre-trained language models to new domains or languages. |
| Approach: | They propose to extend a pre-trained tokenizer by continuing the BPE merge learning process on new data. |
| Outcome: | The proposed method improves tokenization efficiency and improves model utilization. |
ComicScene154: A Scene Dataset for Comic Analysis (2025.emnlp-main)
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| Challenge: | Comics offer compelling yet under-explored domain for computational narrative analysis . authors highlight potential of comics for narrative-driven, multimodal data analysis based on novel comics . |
| Approach: | They propose a dataset of scene-level narrative arcs derived from comic books . they highlight their potential to inform broader research on multimodal storytelling . |
| Outcome: | The dataset provides an initial benchmark that future studies can build upon. |
Model in Distress: Sentiment Analysis on French Synthetic Social Media (2026.findings-acl)
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Pierre-Carl Langlais, Pavel Chizhov, Yannick Detrois, Carlos Rosas Hinostroza, Ivan P. Yamshchikov, Bastien Perroy
| Challenge: | Large companies and services handle a substantial volume of reviews and social media mentions. |
| Approach: | They propose a generalizable pipeline for automated customer feedback analysis using backtranslation and fine-tuned models to generate 1.7 million tweets from a small seed corpus, complemented by synthetic reasoning traces. |
| Outcome: | The proposed pipeline generates 1.7 million synthetic tweets from a small seed corpus, complemented by synthetic reasoning traces. |
Smotrom tvoja på ander drogoj verden! Resurrecting Dead Pidgin with Generative Models: Russenorsk Case Study (2025.findings-acl)
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| Challenge: | Russenorsk is a pidgin language used in trade interactions between Russian and Norwegian speakers. |
| Approach: | They propose to analyze Russenorsk's lexicon using modern large language models . they construct a structured dictionary of the language, grouped by synonyms and word origins . |
| Outcome: | The proposed model can generate linguistic hypotheses about Russenorsk, the authors show . their model can be used to generate renderings of contemporary Russian and Norwegian texts . |
Decomposing Textual Information For Style Transfer (D19-56)
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| Challenge: | Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. |
| Approach: | They propose to use latent representations to effectively decompose different aspects of textual information using a framework of style transfer for texts. |
| Outcome: | The proposed methods show that higher quality representations correlate with higher performance in bilingual evaluation understudy (BLEU) between output and human-written reformulations. |
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites (D19-1)
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| Challenge: | Currently, standard methods for style transfer have several significant problems. |
| Approach: | They propose to take BLEU between input and human-written reformulations into consideration for benchmarks. |
| Outcome: | The proposed architectures outperform state-of-the-art in style transfer metric on human-written reformulations and take BLEU between input and output into consideration for benchmarks. |