We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL'24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.

翻译：本研究将基于开放权重大语言模型的定义生成器应用于新词义解释任务，以目标词汇的用法作为输入。为此，我们采用AXOLOTL'24可解释语义变化建模共享任务中的数据集，该数据集涵盖芬兰语、俄语和德语。我们通过微调开源模型，使其性能优于前述共享任务中采用封闭专有大语言模型的最佳提交结果，并将模型公开提供。此外，我们发现编码器-解码器架构的定义生成器与仅解码器架构的模型表现相当。