用微调大模型预测EvaCun 2025的下一个词，不加特殊处理。
Finetuning LLMs for EvaCun 2025 token prediction shared task
- 直接在任务数据上微调Command-R、Mistral等大模型
- 三种提示方式在预留数据上测试，未做额外优化
- 适合快速验证基础模型在新任务上的表现
本文介绍我们针对EvaCun 2025 token预测任务的提交方案。系统基于Command-R、Mistral和Aya Expanse等大模型，在组织方提供的任务数据上进行微调。由于对任务领域和语言了解有限，我们仅使用原始训练数据，未做任何特定任务的调整、预处理或过滤。我们比较了三种不同提示策略生成预测结果的方法，并在预留的验证集上进行评估。
原文摘要 · Abstract (English)
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.
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