Papers by Olga Kolesnikova
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation (2024.lrec-main)
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Atnafu Lambebo Tonja, Israel Abebe Azime, Tadesse Destaw Belay, Mesay Gemeda Yigezu, Moges Ahmed Ah Mehamed, Abinew Ali Ayele, Ebrahim Chekol Jibril, Michael Melese Woldeyohannis, Olga Kolesnikova, Philipp Slusallek, Dietrich Klakow, Seid Muhie Yimam
| Challenge: | Low-resource languages are lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. |
| Approach: | They propose to use multilingual large language models for five Ethiopian languages and a benchmark dataset to evaluate their performance. |
| Outcome: | The proposed models outperform existing models in five Ethiopian languages and a benchmark dataset for various downstream NLP tasks. |
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding (2025.coling-main)
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Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, Grigori Sidorov, Dietrich Klakow, Philip Slusallek, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Emotion classification is one of the most challenging tasks in large language models. |
| Approach: | They propose to use a multi-label emotion classification dataset for four Ethiopian languages to evaluate their ability to learn and reason. |
| Outcome: | The proposed model improves the understanding of emotions in language models and how people convey emotions through various languages. |
NLP Progress in Indigenous Latin American Languages (2024.naacl-long)
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Atnafu Tonja, Fazlourrahman Balouchzahi, Sabur Butt, Olga Kolesnikova, Hector Ceballos, Alexander Gelbukh, Thamar Solorio
| Challenge: | a new study examines the marginalization of indigenous languages in the face of rapid technological advancements. |
| Approach: | They highlight the cultural richness of indigenous languages and the risk they face of being overlooked in the realm of natural language processing. |
| Outcome: | The authors highlight the cultural richness of indigenous languages and their risk of being overlooked in the realm of natural language processing. |
CULEMO: Cultural Lenses on Emotion - Benchmarking LLMs for Cross-Cultural Emotion Understanding (2025.acl-long)
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Tadesse Destaw Belay, Ahmed Haj Ahmed, Alvin C Grissom Ii, Iqra Ameer, Grigori Sidorov, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Existing emotion benchmarks rely on keyword-based emotion recognition, overlooking cultural dimensions required for emotion understanding. |
| Approach: | They propose a benchmark to evaluate culturally-aware emotion prediction across six languages. |
| Outcome: | The proposed benchmark evaluates state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks. |