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When Many-Shot Prompting Fails: An Empirical Study of LLM Code Translation
68t50artificial-intelligencecomputation-and-languaged-2-7i-2-6i-2-7programming-languagessoftware-engineering
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 empiri
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