提示PaLM进行翻译：评估策略与性能
Prompting PaLM for Translation: Assessing Strategies and Performance
AI总结：
本研究深入评估了PaLM在机器翻译中的能力，发现优化提示中的示例质量是提升few-shot翻译性能的关键，尽管其表现优异但仍不及最先进的监督翻译系统。
摘要：
在多语言但非平行文本上训练的大型语言模型（LLMs）展现出在语言间翻译的卓越能力。我们通过对pathways language model（PaLM）的深入研究来探究这一能力，该模型在迄今同样训练方式的LLMs中展示了最强的机器翻译（MT）性能。我们调查了为few-shot prompting选择翻译示例的各种策略，得出结论：示例质量是最重要的因素。使用优化后的提示，我们借助更新的测试集、现代MT指标和人工评估，重新审视了先前对PaLM的MT能力的评估，发现其性能虽令人瞩目，但仍落后于最先进的监督系统。最后，我们分析了PaLM的MT输出，揭示了一些有趣的特性及未来工作的前景。
英文摘要：
Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages. We probe this ability in an in-depth study of the pathways language model (PaLM), which has demonstrated the strongest machine translation (MT) performance among similarly-trained LLMs to date. We investigate various strategies for choosing translation examples for few-shot prompting, concluding that example quality is the most important factor. Using optimized prompts, we revisit previous assessments of PaLM's MT capabilities with more recent test sets, modern MT metrics, and human evaluation, and find that its performance, while impressive, still lags that of state-of-the-art supervised systems. We conclude by providing an analysis of PaLM's MT output which reveals some interesting properties and prospects for future work.
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。
