The surge of interest in culturally aware and adapted Natural Language
Processing (NLP) has inspired much recent research. However, the lack of common
understanding of the concept of "culture" has made it difficult to evaluate
progress in this emerging area. Drawing on prior research in NLP and related
fields, we propose an extensive taxonomy of elements of culture that can
provide a systematic framework for analyzing and understanding research
progress. Using the taxonomy, we survey existing resources and models for
culturally aware and adapted NLP, providing an overview of the state of the art
and the research gaps that still need to be filled.

文化感知，适应性自然语言处理，文化元素分类，调查资源与模型，研究进展与研究空白。

文化意识和适应性自然语言处理：分类与现状调研

Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State  of the Art

With the increasing integration of AI into everyday life, it's becoming
crucial to design AI systems that serve users from diverse backgrounds by
making them culturally aware. In this paper, we present GD-COMET, a geo-diverse
version of the COMET commonsense inference model. GD-COMET goes beyond Western
commonsense knowledge and is capable of generating inferences pertaining to a
broad range of cultures. We demonstrate the effectiveness of GD-COMET through a
comprehensive human evaluation across 5 diverse cultures, as well as extrinsic
evaluation on a geo-diverse task. The evaluation shows that GD-COMET captures
and generates culturally nuanced commonsense knowledge, demonstrating its
potential to benefit NLP applications across the board and contribute to making
NLP more inclusive.

通过人类评估和地理多样性任务的外在评估，我们展示了 GD-COMET 的有效性，证明其具有捕捉和生成跟文化相关的常识知识的潜力，从而有望在 NLP 应用中产生广泛影响，并有助于使 NLP 更加包容。

GD-COMET：一种地理多样化常识推理模型

GD-COMET: A Geo-Diverse Commonsense Inference Model

This paper explores the imperative need and methodology for developing a
localized Large Language Model (LLM) tailored for Arabic, a language with
unique cultural characteristics that are not adequately addressed by current
mainstream models like ChatGPT. Key concerns additionally arise when
considering cultural sensitivity and local values. To this end, the paper
outlines a packaged solution, including further pre-training with Arabic texts,
supervised fine-tuning (SFT) using native Arabic instructions and GPT-4
responses in Arabic, and reinforcement learning with AI feedback (RLAIF) using
a reward model that is sensitive to local culture and values. The objective is
to train culturally aware and value-aligned Arabic LLMs that can serve the
diverse application-specific needs of Arabic-speaking communities.
Extensive evaluations demonstrated that the resulting LLM called
`\textbf{AceGPT}' is the SOTA open Arabic LLM in various benchmarks, including
instruction-following benchmark (i.e., Arabic Vicuna-80 and Arabic AlpacaEval),
knowledge benchmark (i.e., Arabic MMLU and EXAMs), as well as the
newly-proposed Arabic cultural \& value alignment benchmark. Notably, AceGPT
outperforms ChatGPT in the popular Vicuna-80 benchmark when evaluated with
GPT-4, despite the benchmark's limited scale. % Natural Language Understanding
(NLU) benchmark (i.e., ALUE)
Codes, data, and models are in this https URL

本文探讨了为阿拉伯语开发本土化大型语言模型（LLM）的必要性和方法论，介绍了一种完整的解决方案，包括使用阿拉伯语文本进行进一步的预训练，使用阿拉伯语指令和阿拉伯语回复进行监督微调（SFT），以及使用对本土文化和价值观敏感的奖励模型进行强化学习和 AI 反馈（RLAIF）。通过全面的评估，证明了该模型 AceGPT 在多个基准测试中是领先的，并且在阿拉伯语相关的文化和价值观方面表现出色。