九个 LLM 在翻译中的词汇知识冲突实验：提示词框架影响上下文知识依赖
Are These Programmers Ripe Enough to Eat? Evaluating How LLMs Handle Lexical Knowledge Conflicts in Translation
用中文译英文的任务测了九个模型听不听话：让它们按提示词的新词义翻译，七个模型照样按旧知识来。做翻译工作流的可以看看哪些条件能压住模型的固执。
论文构建了一个中文到英文的受控翻译任务，把提示词提供的新词义作为上下文知识（CK），系统评估词频、句子和提示框架对九个模型输出的影响。结果显示低频词和支撑性句子会提高 CK 依赖，且九个模型中有七个即使被明确要求忽略提供的释义仍偏向使用它。同家族内更大的模型更倾向利用 CK。后续三个实验表明模态削弱可降低依赖，而重复效果取决于提示布局。
Are These Programmers Ripe Enough to Eat? Evaluating How LLMs Handle Lexical Knowledge Conflicts in Translation
External lexical resources can help large language models (LLMs) translate texts in low-resource languages and specialized domains, but these resources may conflict with models' prior lexical knowledge. To isolate this conflict, we construct a controlled Mandarin-to-English translation task in which familiar source words are assigned new meanings within and across semantic categories. We treat these prompt-supplied meanings as contextual knowledge (CK) and systematically evaluate the effects of word frequency, sentence context, and prompt framing on the translated outputs. Across nine models, CK reliance is greater for less frequent source words and in sentence contexts that support the supplied meanings. Prompt framing can reduce CK reliance, but seven of nine models favor CK even when explicitly instructed to ignore the supplied meanings. Within model families, larger models are more likely than smaller models to utilize CK in their outputs. Three follow-up experiments further show that modal weakening lowers CK reliance, the effect of repetition depends on prompt layout, and the effect of reasoning varies across models. Overall, our controlled study reveals how CK reliance varies across conditions and models, motivating new methods to detect and evaluate lexical conflicts and guide models' lexical choices in LLM-based translation.
