当多示例提示失效：对LLM代码翻译的实证研究
When Many-Shot Prompting Fails: An Empirical Study of LLM Code Translation
AI总结：
本研究发现代码翻译中少量精心挑选的示例比大量示例更有效，挑战了ICL中'更多更好'的普遍假设。
摘要：
大型语言模型（LLM）拥有庞大的上下文窗口，为上下文学习（ICL）提供了新的途径，其中提供大量示例（
英文摘要：
Large Language Models (LLMs) with vast context windows offer new avenues for in-context learning (ICL), where providing many examples ("many-shot" prompting) is often assumed to enhance performance. We investigate this assumption for the complex task of code translation. Through a large-scale empirical study of over 90,000 translations, we systematically evaluate the impact of scaling in-context examples from zero-shot to many-shot configurations of up to 625 examples, with prompts spanning from approximately 100,000 to 800,000 tokens. Our findings reveal a "many-shot paradox": while static similarity metrics may modestly improve with more examples, functional correctness consistently peaks with few-shot prompting (5-25 examples). Providing substantially more examples often degrades this crucial functional performance. This study highlights that for code translation, the quality of a few well-chosen examples outweighs sheer quantity, challenging the universal efficacy of "more is better" for ICL and underscoring the task-dependent nature of optimal prompting strategies. Our results have significant implications for effectively leveraging LLMs in software engineering.
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。
