用小模型和大模型结合的方法，实现跨20种语言的假言标准化，效果领先。
AKCIT-FN at CheckThat! 2025: Switching Fine-Tuned SLMs and LLM Prompting for Multilingual Claim Normalization
- 对高资源语言用微调小模型，零样本语言用大模型提示策略
- 在20种语言中15种进入前三，8种零样本语言获第二名
- 适合需要多语言事实核查的系统开发者参考
假言标准化是将非正式社交媒体内容转化为简洁自洽陈述的关键步骤，对自动化事实核查至关重要。本文介绍我们在CLEF-2025 CheckThat!任务2中的参赛方案，该任务要求在20种语言上进行假言标准化，分为13个有监督（高资源）和7个零样本（无训练数据）赛道。我们的方法利用微调的小语言模型处理有监督语言，采用大语言模型提示策略应对零样本场景，在20种语言中15种进入前三名，其中8种为零样本语言，且获得第二名。以葡萄牙语为例，系统平均METEOR得分为0.5290，排名第三。所有代码与配置已公开于https://github.com/ju-resplande/checkthat2025_normalization。
原文摘要 · Abstract (English)
Claim normalization, the transformation of informal social media posts into concise, self-contained statements, is a crucial step in automated fact-checking pipelines. This paper details our submission to the CLEF-2025 CheckThat! Task~2, which challenges systems to perform claim normalization across twenty languages, divided into thirteen supervised (high-resource) and seven zero-shot (no training data) tracks. Our approach, leveraging fine-tuned Small Language Models (SLMs) for supervised languages and Large Language Model (LLM) prompting for zero-shot scenarios, achieved podium positions (top three) in fifteen of the twenty languages. Notably, this included second-place rankings in eight languages, five of which were among the seven designated zero-shot languages, underscoring the effectiveness of our LLM-based zero-shot strategy. For Portuguese, our initial development language, our system achieved an average METEOR score of 0.5290, ranking third. All implementation artifacts, including inference, training, evaluation scripts, and prompt configurations, are publicly available at https://github.com/ju-resplande/checkthat2025_normalization.
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