Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages
- URL: http://arxiv.org/abs/2405.20037v1
- Date: Thu, 30 May 2024 13:19:16 GMT
- Title: Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages
- Authors: Taichi Murayama, Kunihiro Miyazaki, Yasuko Matsubara, Yasushi Sakurai,
- Abstract summary: We analyzed over 6.8 million tweets in 14 different languages.
Our findings reveal a global trend in the perception of generative AI, accompanied by language-specific nuances.
- Score: 6.278517495094834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The advent of generative AI tools has had a profound impact on societies globally, transcending geographical boundaries. Understanding these tools' global reception and utilization is crucial for service providers and policymakers in shaping future policies. Therefore, to unravel the perceptions and engagements of individuals within diverse linguistic communities with regard to generative AI tools, we extensively analyzed over 6.8 million tweets in 14 different languages. Our findings reveal a global trend in the perception of generative AI, accompanied by language-specific nuances. While sentiments toward these tools vary significantly across languages, there is a prevalent positive inclination toward Image tools and a negative one toward Chat tools. Notably, the ban of ChatGPT in Italy led to a sentiment decline and initiated discussions across languages. Furthermore, we established a taxonomy for interactions with chatbots, creating a framework for social analysis underscoring variations in generative AI usage among linguistic communities. We find that the Chinese community predominantly employs chatbots as substitutes for search, while the Italian community tends to present more intricate prompts. Our research provides a robust foundation for further explorations of the social dynamics surrounding generative AI tools and offers invaluable insights for decision-makers in policy, technology, and education.
Related papers
- Harnessing the Power of Artificial Intelligence to Vitalize Endangered Indigenous Languages: Technologies and Experiences [31.62071644137294]
We discuss the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP.
We report encouraging results in the development of high-quality machine learning translators for Indigenous languages.
We present prototypes we have built in projects done in 2023 and 2024 with Indigenous communities in Brazil, aimed at facilitating writing.
arXiv Detail & Related papers (2024-07-17T14:46:37Z) - Language Model Alignment in Multilingual Trolley Problems [138.5684081822807]
Building on the Moral Machine experiment, we develop a cross-lingual corpus of moral dilemma vignettes in over 100 languages called MultiTP.
Our analysis explores the alignment of 19 different LLMs with human judgments, capturing preferences across six moral dimensions.
We discover significant variance in alignment across languages, challenging the assumption of uniform moral reasoning in AI systems.
arXiv Detail & Related papers (2024-07-02T14:02:53Z) - Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal.
We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
arXiv Detail & Related papers (2024-04-17T02:57:42Z) - Distributed agency in second language learning and teaching through generative AI [0.0]
ChatGPT can provide informal second language practice through chats in written or voice forms.
Instructors can use AI to build learning and assessment materials in a variety of media.
arXiv Detail & Related papers (2024-03-29T14:55:40Z) - Factuality Challenges in the Era of Large Language Models [113.3282633305118]
Large Language Models (LLMs) generate false, erroneous, or misleading content.
LLMs can be exploited for malicious applications.
This poses a significant challenge to society in terms of the potential deception of users.
arXiv Detail & Related papers (2023-10-08T14:55:02Z) - Learning to Model the World with Language [100.76069091703505]
To interact with humans and act in the world, agents need to understand the range of language that people use and relate it to the visual world.
Our key idea is that agents should interpret such diverse language as a signal that helps them predict the future.
We instantiate this in Dynalang, an agent that learns a multimodal world model to predict future text and image representations.
arXiv Detail & Related papers (2023-07-31T17:57:49Z) - Towards Bridging the Digital Language Divide [4.234367850767171]
multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages.
We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented.
We present a new initiative that aims at reducing linguistic bias through both technological design and methodology.
arXiv Detail & Related papers (2023-07-25T10:53:20Z) - Systematic Review for AI-based Language Learning Tools [0.0]
This review synthesized information on AI tools that were developed between 2017 and 2020.
A majority of these tools utilized machine learning and natural language processing.
After using these tools, learners demonstrated gains in their language abilities and knowledge.
arXiv Detail & Related papers (2021-10-29T11:54:51Z) - Systematic Inequalities in Language Technology Performance across the
World's Languages [94.65681336393425]
We introduce a framework for estimating the global utility of language technologies.
Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies and more linguistic NLP tasks.
arXiv Detail & Related papers (2021-10-13T14:03:07Z) - SocialAI 0.1: Towards a Benchmark to Stimulate Research on
Socio-Cognitive Abilities in Deep Reinforcement Learning Agents [23.719833581321033]
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI.
Current approaches focus on language as a communication tool in very simplified and non diverse social situations.
We argue that aiming towards human-level AI requires a broader set of key social skills.
arXiv Detail & Related papers (2021-04-27T14:16:29Z) - Can You be More Social? Injecting Politeness and Positivity into
Task-Oriented Conversational Agents [60.27066549589362]
Social language used by human agents is associated with greater users' responsiveness and task completion.
The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element.
Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
arXiv Detail & Related papers (2020-12-29T08:22:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.