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Tatiana Shavrina

6 accepted papers

2025

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench

Edan Toledo, Karen Hambardzumyan, Martin Josifoski, RISHI HAZRA, Nicolas Baldwin, Alexis Audran-Reiss +19
NeurIPS 2025spotlight

AI research agents are demonstrating great potential to accelerate scientific progress by automating the design, implementation, and training of machine learning models. We focus on methods for improving agents' performance on MLE-bench, a challenging benchmark where agents compete in Kaggle competi…

Cited by 0Source ↗code ↗Scholar ↗
2025

Combining Code Generating Large Language Models and Self-Play to Iteratively Refine Strategies in Games

Yoram Bachrach, Edan Toledo, Karen Hambardzumyan, Despoina Magka, Martin Josifoski, Minqi Jiang +8
IJCAI 2025

We propose a self-play approach to generating strategies for playing in multi-player games, where strategies are represented as computer code. We use large language models (LLMs) to generate pieces of code to play in the game, which we refer to as generated bots. We engage the LLM generated bots in

Cited by 0Source ↗PDF ↗Scholar ↗
2025

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements

Bingchen Zhao, Despoina Magka, Minqi Jiang, Xian Li, Roberta Raileanu, Tatiana Shavrina +17
NeurIPS 2025poster

Rapidly improving large language models (LLMs) have the potential to assist in scientific progress. One critical skill in this endeavor is the ability to faithfully reproduce existing work. To evaluate the capability of AI agents to reproduce complex code in an active research area, we introduce the…

Cited by 0Source ↗code ↗Scholar ↗
2024

A Family of Pretrained Transformer Language Models for Russian

Dmitry Zmitrovich, Aleksandr Abramov, Andrey Kalmykov, Vitaly Kadulin, Maria Tikhonova, Ekaterina Taktasheva +7
COLING 2024main

Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans en…

Cited by 59Source ↗PDF ↗code ↗Scholar ↗
2024

EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas

Mikhail Mozikov, Nikita Severin, Valeria Bodishtianu, Maria Glushanina, Ivan Nasonov, Daniil Orekhov +9
NeurIPS 2024poster

One of the urgent tasks of artificial intelligence is to assess the safety and alignment of large language models (LLMs) with human behavior. Conventional verification only in pure natural language processing benchmarks can be insufficient. Since emotions often influence human decisions, this paper…

Cited by 2Source ↗PDF ↗Scholar ↗
2022

TAPE: Assessing Few-shot Russian Language Understanding

Ekaterina Taktasheva, Alena Fenogenova, Denis Shevelev, Nadezhda Katricheva, Maria Tikhonova, Albina Akhmetgareeva +8
EMNLP 2022finding

Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes. However, this fast-growing area lacks standardized evaluation suites for non-English languages, hindering progress outside the Anglo-centric paradigm. To address this line of researc…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗