无需训练数据，大模型直接生成词形还原结果，效果超越传统方法。
Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data
- 不依赖特定领域或语言的标注数据，用少量示例直接提示大模型生成词形还原结果。
- 在12种不同形态复杂度的语言中，多数语言上表现优于传统微调模型。
- 适合无标注数据资源的低资源语言处理任务，尤其适用于快速部署场景。
词形还原是将文本中的词汇转换为其词典形式的任务。尽管大型语言模型（LLMs）在众多自然语言处理任务中表现出色，但其在上下文词形还原任务中的表现尚无充分证据。本文实证研究了最新一代LLMs在上下文词形还原中的能力，与传统的全监督方法进行对比。特别地，我们考察了目标领域或语言缺乏标注数据时的情况，比较了（i）基于编码器的监督方法（在非目标领域上微调），以及（ii）跨语言方法，与直接使用大模型进行上下文提示生成词形还原结果的效果。实验覆盖12种形态复杂度不同的语言，结果显示：尽管编码器模型在微调后仍具竞争力，但当前大模型仅需少数示例即可实现上下文直接生成，在大多数语言上达到最先进水平。数据与代码将于发表后公开：https://github.com/oltoporkov/lemma-dilemma
原文摘要 · Abstract (English)
Lemmatization is the task of transforming all words in a given text to their dictionary forms. While large language models (LLMs) have demonstrated their ability to achieve competitive results across a wide range of NLP tasks, there is no prior evidence of how effective they are in the contextual lemmatization task. In this paper, we empirically investigate the capacity of the latest generation of LLMs to perform in-context lemmatization, comparing it to the traditional fully supervised approach. In particular, we consider the setting in which supervised training data is not available for a target domain or language, comparing (i) encoder-only supervised approaches, fine-tuned out-of-domain, and (ii) cross-lingual methods, against direct in-context lemma generation with LLMs. Our experimental investigation across 12 languages of different morphological complexity finds that, while encoders remain competitive in out-of-domain settings when fine-tuned on gold data, current LLMs reach state-of-the-art results for most languages by directly generating lemmas in-context without prior fine-tuning, provided just with a few examples. Data and code available upon publication: https://github.com/oltoporkov/lemma-dilemma
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