让AI生成更符合不同文化背景的绘画描述，提升跨文化理解力。
Pragmatics Meets Culture: Culturally-adapted Artwork Description Generation and Evaluation
- 基于文化语境设计对话式描述生成方法
- 模型理解力提升8.2%，人类评估帮助度提高8.0%
- 适合跨文化传播、AI内容本地化研究者
语言模型在决策任务中表现出多种文化偏见，但在开放文本生成任务中的文化熟悉度仍不清楚。本文提出文化适应性艺术描述生成任务，要求模型为不同文化背景的受众描述艺术品，这些受众对作品中嵌入的文化符号与叙事的熟悉程度各异。为评估此任务中的文化胜任力，我们提出基于文化基础问答的评估框架。结果表明，基础模型在此任务中仅略有效，而通过引入实用型说话者模型，可使模拟听众的理解度提升最高达8.2%。人工评估进一步证实，具备更高实用能力的模型被评价为更有助于理解，帮助度提升8.0%。
原文摘要 · Abstract (English)
Language models are known to exhibit various forms of cultural bias in decision-making tasks, yet much less is known about their degree of cultural familiarity in open-ended text generation tasks. In this paper, we introduce the task of culturally-adapted art description generation, where models describe artworks for audiences from different cultural groups who vary in their familiarity with the cultural symbols and narratives embedded in the artwork. To evaluate cultural competence in this pragmatic generation task, we propose a framework based on culturally grounded question answering. We find that base models are only marginally adequate for this task, but, through a pragmatic speaker model, we can improve simulated listener comprehension by up to 8.2%. A human study further confirms that the model with higher pragmatic competence is rated as more helpful for comprehension by 8.0%.
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