Papers by Max Ryabinin
It’s All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning (2021.findings-acl)
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| Challenge: | gilbert et al.: commonsense reasoning is a key problem in natural language processing but its capabilities are still unstudied. gilland eetal.: a new approach to commonsensible reasoning is needed to solve the problem. |
| Approach: | They propose a method which trains a linear classifier with weights of multi-head attention as features and a multilingual Winograd Schema corpus to measure cross-lingual generalization ability. |
| Outcome: | The proposed approach performs competitively with recent approaches even when applied to other languages in a zero-shot manner. |
Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements (2024.findings-acl)
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| Challenge: | Large language models demonstrate remarkable ability for learning to solve new tasks from a few examples. |
| Approach: | They propose to use templates to aggregate model predictions across multiple templates to improve model performance. |
| Outcome: | The proposed model ensembles boost model predictions while being robust to the choice of random set of templates. |
Embedding Words in Non-Vector Space with Unsupervised Graph Learning (2020.emnlp-main)
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| Challenge: | GraphGlove is an unsupervised graph word representations that are learned end-to-end. |
| Approach: | They propose a method to learn weighted graph word representations end-to-end using a weighteable weighte . they adopt a hierarchical graph representation method and modify the GloVe training algorithm to learn graph representations. |
| Outcome: | The proposed method outperforms vector-based methods on word similarity and analogy tasks. |
Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)
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Dmitry Popov, Vladislav Negodin, Ekaterina Enikeeva, Iana Matrosova, Nikolay Karpachev, Max Ryabinin
| Challenge: | Currently, traditional evaluation methods struggle to detect subtle translation errors. |
| Approach: | They propose to use a dataset of human evaluations for English–Russian translations created by professional linguists to enable consistent and rich annotation. |
| Outcome: | The proposed protocol allows expert assessments without time pressure to yield substantially different results from standard evaluations. |
Multilingual Language Model Pretraining using Machine-translated Data (2025.emnlp-main)
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Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Ifeoluwa Adelani, Yihong Chen, Raphael Tang, Pontus Stenetorp
| Challenge: | Existing methods for collecting and filtering multilingual web data lead to most languages lagging behind English performance due to the Internet's English-centric nature. |
| Approach: | They propose to translate a high-quality English web corpus into nine languages and pretrain a 1.3B-parameter model on it. |
| Outcome: | The proposed model matches or outperforms multilingual LLMs of similar size across Non-English understanding and reasoning tasks despite being trained on an order of magnitude less data. |
RuCoLA: Russian Corpus of Linguistic Acceptability (2022.emnlp-main)
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Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin, Alena Pestova, Ivan Smurov, Ekaterina Artemova
| Challenge: | Recent research has focused on evaluating the grammatical knowledge of language models with acceptability judgments. |
| Approach: | They propose to build a corpus of linguistic acceptability in Russian using a binary LA approach. |
| Outcome: | The proposed set of tests shows that the most widely used language models still fall behind humans by a large margin when detecting morphological and semantic errors. |