SandboxAQ 提交给 MRL 2024 多语言多任务信息检索共享任务的参赛方案
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
AI总结：
本研究针对五种语言的问答与命名实体识别任务，测试五种大语言模型搭配多种提示方法的效果，发现模型表现随任务、语言差异显著，凸显多语言NLP需任务特定方法。
摘要：
本文探究了五种不同语言下的问答（QA）与命名实体识别（NER）问题。我们测试了五种大语言模型，搭配零样本、思维链推理、翻译技术等多种提示方法。结果显示，部分模型表现始终优于其他模型，但它们的效果在不同任务和语言间差异显著。我们发现高级提示技术通常能提升 QA 性能，但对 NER 的效果好坏参半；同时观察到语言难度模式在不同任务间存在差异。这些发现凸显了多语言自然语言处理中需要针对特定任务设计方法，也表明当前模型可能在不同任务上发展出不同的语言能力。
英文摘要：
This paper explores the problems of Question Answering (QA) and Named Entity Recognition (NER) in five diverse languages. We tested five Large Language Models with various prompting methods, including zero-shot, chain-of-thought reasoning, and translation techniques. Our results show that while some models consistently outperform others, their effectiveness varies significantly across tasks and languages. We saw that advanced prompting techniques generally improved QA performance but had mixed results for NER; and we observed that language difficulty patterns differed between tasks. Our findings highlight the need for task-specific approaches in multilingual NLP and suggest that current models may develop different linguistic competencies for different tasks.
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。
