Papers by Katerina Kolomeytseva

2 papers
A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages (2024.findings-eacl)

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Challenge: Recent advances in large language models have shown impressive text generation and language understanding capabilities, evident in benchmarks like SuperGLUE, GEM, BigBench etc.
Approach: They propose a method for generative spelling correction that can be extended to any language with minor changes.
Outcome: The proposed method can be extended to any language with minor changes, and is based on a set of generative models with a single-domain and multi-domain test sets.
MERA: A Comprehensive LLM Evaluation in Russian (2024.acl-long)

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Challenge: Recent advances in foundation models have led to the emergence of powerful Large Language Models (LLMs), which showcase unprecedented tasksolving capabilities.
Approach: They propose a method to evaluate FMs and LMs in fixed zero- and few-shot instruction settings that can be extended to other modalities.
Outcome: The proposed evaluation methodology includes an open-source code base and a leaderboard with a submission system.

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