用开源大模型生成新词义解释，效果优于闭源模型。
Explaining novel senses using definition generation with open language models
- 基于开源大模型的定义生成器，输入新词用法自动生成解释。
- 在芬兰语、俄语、德语上表现超越原比赛最优闭源模型。
- 编码器-解码器结构与仅解码器结构性能相当，适合可复现研究。
我们利用基于开源权重的大语言模型构建的定义生成器，针对新词义解释任务，以目标词的使用语境作为输入。实验采用 AXOLOTL'24 共享任务提供的芬兰语、俄语和德语数据集。通过微调，我们公开了性能优于该任务最佳闭源模型提交结果的开源模型。此外，我们发现编码器-解码器型定义生成器在性能上与仅解码器型模型相当。
原文摘要 · Abstract (English)
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.
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