LLMs are increasingly being deployed for multilingual applications and have
demonstrated impressive translation capabilities between several low and high
resource languages. An aspect of translation that often gets overlooked is that
of cultural adaptation, or modifying source culture references to suit the
target culture. Cultural adaptation has applications across several creative
industries and requires intimate knowledge of source and target cultures during
translation. While specialized translation models still outperform LLMs on the
machine translation task when viewed from the lens of correctness, they are not
sensitive to cultural differences often requiring manual correction. LLMs on
the other hand have a rich reservoir of cultural knowledge embedded within its
parameters that can be potentially exploited for such applications. In this
paper we define the task of cultural adaptation and create an evaluation
framework to benchmark different models for this task. We evaluate the
performance of modern LLMs for cultural adaptation and analyze their cross
cultural knowledge while connecting related concepts across different cultures.
We also analyze possible issues with automatic adaptation including cultural
biases and stereotypes. We hope that this task will offer more insight into the
cultural understanding of LLMs and their creativity in cross-cultural
scenarios.

该研究定义了文化适应的任务，并创建了一个评估框架来评估不同模型在这个任务上的性能。研究评估了现代 LLMs 在文化适应方面的表现，分析了它们在不同文化之间连接相关概念时的跨文化知识，并分析了自动适应中可能存在的文化偏见和刻板印象。希望该任务能够更深入地了解 LLMs 在跨文化情境中的文化理解和创造力。

跨文化翻译：针对语内文化适应的法学硕士

Translating Across Cultures: LLMs for Intralingual Cultural Adaptation

Building upon the considerable advances in Large Language Models (LLMs), we
are now equipped to address more sophisticated tasks demanding a nuanced
understanding of cross-cultural contexts. A key example is recipe adaptation,
which goes beyond simple translation to include a grasp of ingredients,
culinary techniques, and dietary preferences specific to a given culture. We
introduce a new task involving the translation and cultural adaptation of
recipes between Chinese and English-speaking cuisines. To support this
investigation, we present CulturalRecipes, a unique dataset comprised of
automatically paired recipes written in Mandarin Chinese and English. This
dataset is further enriched with a human-written and curated test set. In this
intricate task of cross-cultural recipe adaptation, we evaluate the performance
of various methods, including GPT-4 and other LLMs, traditional machine
translation, and information retrieval techniques. Our comprehensive analysis
includes both automatic and human evaluation metrics. While GPT-4 exhibits
impressive abilities in adapting Chinese recipes into English, it still lags
behind human expertise when translating English recipes into Chinese. This
underscores the multifaceted nature of cultural adaptations. We anticipate that
these insights will significantly contribute to future research on
culturally-aware language models and their practical application in culturally
diverse contexts.

我们介绍了一项新任务，涉及中英文烹饪食谱之间的翻译和文化适应，使用了 CulturalRecipes 数据集并评估了各种方法的性能，包括 GPT-4 和其他 LLMs，传统机器翻译以及信息检索技术。在跨文化烹饪适应这一复杂任务中，我们发现 GPT-4 在将中文食谱转换为英文时表现出色，但在将英文食谱翻译为中文时仍然不及人类专业知识，凸显了文化适应的多方面性质。我们预计这些见解将对未来研究文化感知语言模型及其在文化多样性环境中的实际应用方面有重要贡献。

食谱的文化适应

Cultural Adaptation of Recipes

The recent release of ChatGPT has garnered widespread recognition for its
exceptional ability to generate human-like responses in dialogue. Given its
usage by users from various nations and its training on a vast multilingual
corpus that incorporates diverse cultural and societal norms, it is crucial to
evaluate its effectiveness in cultural adaptation. In this paper, we
investigate the underlying cultural background of ChatGPT by analyzing its
responses to questions designed to quantify human cultural differences. Our
findings suggest that, when prompted with American context, ChatGPT exhibits a
strong alignment with American culture, but it adapts less effectively to other
cultural contexts. Furthermore, by using different prompts to probe the model,
we show that English prompts reduce the variance in model responses, flattening
out cultural differences and biasing them towards American culture. This study
provides valuable insights into the cultural implications of ChatGPT and
highlights the necessity of greater diversity and cultural awareness in
language technologies.

本文通过分析 ChatGPT 对量化人类文化差异的问题的反应，研究了 ChatGPT 的文化背景，并表明当提示语是美国背景时，ChatGPT 的响应与美国文化强烈关联，但对于其他文化背景适应性降低；此外，使用不同提示来探测模型，显示英文提示会减少模型响应的差异，使文化差异变平，并偏向美国文化，该研究提供了有关 ChatGPT 文化影响的有价值见解，并强调了语言技术中更大的多样性和文化意识的必要性。