Finetuning LLMs for EvaCun 2025 token prediction shared task
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908599522951168 |
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| author | Jon, Josef Bojar, Ondřej |
| author_facet | Jon, Josef Bojar, Ondřej |
| contents | In this paper, we present our submission for the token prediction task of EvaCun 2025. Our sys-tems are based on LLMs (Command-R, Mistral, and Aya Expanse) fine-tuned on the task data provided by the organizers. As we only pos-sess a very superficial knowledge of the subject field and the languages of the task, we simply used the training data without any task-specific adjustments, preprocessing, or filtering. We compare 3 different approaches (based on 3 different prompts) of obtaining the predictions, and we evaluate them on a held-out part of the data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Finetuning LLMs for EvaCun 2025 token prediction shared task Jon, Josef Bojar, Ondřej Computation and Language In this paper, we present our submission for the token prediction task of EvaCun 2025. Our sys-tems are based on LLMs (Command-R, Mistral, and Aya Expanse) fine-tuned on the task data provided by the organizers. As we only pos-sess a very superficial knowledge of the subject field and the languages of the task, we simply used the training data without any task-specific adjustments, preprocessing, or filtering. We compare 3 different approaches (based on 3 different prompts) of obtaining the predictions, and we evaluate them on a held-out part of the data. |
| title | Finetuning LLMs for EvaCun 2025 token prediction shared task |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.15561 |