Papers by Evelina Bakhturina
Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning (2026.eacl-long)
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Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, Seungju Han, Ying Lin, Evelina Bakhturina, Eric Nyberg, Yejin Choi, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Prior work has successfully applied Reinforcement Learning (RL) to mathematical reasoning, but generalization to broader domains remains challenging due to limited data and lack of verifiable rewards for unstructured domains. |
| Approach: | They propose a framework that integrates multi-domain corpora into RL training to improve generalization across diverse reasoning tasks. |
| Outcome: | The proposed framework improves generalization across diverse reasoning tasks. |
SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models (2025.naacl-industry)
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| Challenge: | Typical evaluations of Large Language Models (LLMs) report a single accuracy metric per dataset, often derived from an optimized setup. |
| Approach: | They propose a framework for non-adversarial evaluation of large language models that evaluates models by repeatedly testing them on the same benchmarks in various setups. |
| Outcome: | The proposed framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency. |
BioMegatron: Larger Biomedical Domain Language Model (2020.emnlp-main)
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Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, Raghav Mani
| Challenge: | Existing studies on domain language models do not study the factors affecting performance on domain languages. |
| Approach: | They empirically evaluate factors that can affect performance on domain language applications . sub-word vocabulary set, model size, pre-training corpus, and domain transfer are important . |
| Outcome: | The results show language models trained on biomedical text perform better on biomedicine benchmarks than those trained on general domain text corpora. |