测试5种大模型在5种语言上的问答与实体识别表现
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
- 对比零样本、思维链等提示方法在多语言任务中的效果
- 先进提示提升问答性能，但对实体识别效果不一
- 不同语言在各任务中难度差异明显，需定制化方案
本文研究了五种不同语言下的问答（QA）与命名实体识别（NER）任务。我们测试了五种大型语言模型，采用零样本、思维链推理和翻译技术等多种提示方法。结果表明，尽管某些模型整体表现更优，但其效果在不同任务和语言间差异显著。先进提示方法普遍提升了问答性能，但在实体识别上表现参差；同时观察到不同任务中语言难度模式存在差异。研究强调多语言NLP中应采取任务定制化策略，并暗示当前模型对不同任务可能发展出差异化的语言能力。
原文摘要 · Abstract (English)
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.
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