Controlling Translation Purpose through Prompt Engineering: A Skopos-Based Study of AI Poetry Translation

Abstract

Large language models (LLMs) have rapidly expanded the horizon of AI-assisted literary translation, yet the mechanisms of how prompts construct translation purpose are theoretically underexplored. This study, based on Vermeer’s Skopos Theory, conceptualizes prompts as a “digital translation brief” and examines the systematic regulation of four function-oriented prompts: baseline (P0), reader-oriented (P1), form-oriented (P2), and culture-oriented (P3) in DeepSeek’s translation strategies for English-to-Indonesian poetry translation, with Emily Dickinson’s Hope is the Thing with Feathers as the source text. The study finds, through comparative close reading across the four prompt conditions, as well as against two published human translations, that each prompt orientation produces distinct and observable shifts in diction, imagery construction and formal expression: P1 sacrifices collocational stability for literary register and emotional intensity at the expense of; P2 replicates structure but risks over-compliance which undermines target-reader adequacy; P3 allows discourse-level metaphorical coordination and selective dependence on the source-text. Human translators’ choices emerge from cultural memory, aesthetic intentionality, and poetic agency. DeepSeek’s functional adaptability is largely a matter of probabilistic generation and prompt compliance. The findings re-conceptualize prompts not only as technical input instructions, but as purposive regulators of translation, offering a translation-theoretic framework for analysing LLM behaviour and practical guidance for designing Skopos-informed prompts for AI-assisted literary translation.