UWBa 在 SemEval-2025 任务 7：多语言和跨语言的事实核查声明检索
UWBa at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
AI总结：
本文提出零样本事实核查声明检索系统，采用多个大型语言模型获取文本嵌入并测量余弦相似度，仅使用英文翻译作为输入，在单语言和跨语言子任务中分获第7和第9名。
摘要：
本文提出了一个用于事实核查声明检索的零样本系统。我们采用了多个最先进的大型语言模型来获取文本嵌入。然后将这些模型组合以获得最佳结果。我们的方法在单语言子任务中获得第 7 名，在跨语言子任务中获得第 9 名。由于多语言模型未能取得令人满意的结果，我们仅使用英文翻译作为文本嵌入模型的输入。我们通过利用嵌入并测量余弦相似度来识别每个帖子最相关的声明。总体而言，NVIDIA NV-Embed-v2 模型获得了最佳结果。对于某些语言，我们从模型组合（NV-Embed & GPT 或 Mistral）中受益。
英文摘要：
This paper presents a zero-shot system for fact-checked claim retrieval. We employed several state-of-the-art large language models to obtain text embeddings. The models were then combined to obtain the best possible result. Our approach achieved 7th place in monolingual and 9th in cross-lingual subtasks. We used only English translations as an input to the text embedding models since multilingual models did not achieve satisfactory results. We identified the most relevant claims for each post by leveraging the embeddings and measuring cosine similarity. Overall, the best results were obtained by the NVIDIA NV-Embed-v2 model. For some languages, we benefited from model combinations (NV-Embed & GPT or Mistral).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
