語言模型中的文化意識調查：文字與更多

Survey of Cultural Awareness in Language Models: Text and Beyond

October 30, 2024

作者: Siddhesh Pawar, Junyeong Park, Jiho Jin, Arnav Arora, Junho Myung, Srishti Yadav, Faiz Ghifari Haznitrama, Inhwa Song, Alice Oh, Isabelle Augenstein

cs.AI

摘要

在各種應用中大規模部署大型語言模型（LLMs），例如聊天機器人和虛擬助手，需要LLMs對用戶具有文化敏感性，以確保包容性。文化在心理學和人類學中得到廣泛研究，最近在使LLMs更具文化包容性方面出現了激增的研究，這超越了多語性，並建立在心理學和人類學研究成果的基礎上。在本文中，我們調查了將文化意識融入基於文本和多模式LLMs的努力。我們首先通過從人類學和心理學中的文化定義作為出發點，來定義LLMs中的文化意識。然後，我們檢視了用於創建跨文化數據集的方法論，文化包容性在下游任務中的策略，以及用於在LLMs中基準文化意識的方法論。此外，我們討論了文化對齊的道德影響，人機交互在推動LLMs中文化包容性方面的作用，以及文化對齊在推動社會科學研究中的作用。最後，我們根據我們對文獻中存在的空白的發現，提供了未來研究的指引。

English

Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive in LLMs that goes beyond multilinguality and builds on findings from psychology and anthropology. In this paper, we survey efforts towards incorporating cultural awareness into text-based and multimodal LLMs. We start by defining cultural awareness in LLMs, taking the definitions of culture from anthropology and psychology as a point of departure. We then examine methodologies adopted for creating cross-cultural datasets, strategies for cultural inclusion in downstream tasks, and methodologies that have been used for benchmarking cultural awareness in LLMs. Further, we discuss the ethical implications of cultural alignment, the role of Human-Computer Interaction in driving cultural inclusion in LLMs, and the role of cultural alignment in driving social science research. We finally provide pointers to future research based on our findings about gaps in the literature.