Background: Young people spend hours on social media every day, where following influencers is the norm. Influencers post frequently about alcohol and alcohol brands and there has been a rise in influencer marketing. However, prevalence estimates rely on manual analyses of only a small number of posts. We used a large language model (LLM) to estimate at scale how often top Australian Instagram influencers reference alcohol and alcohol brands, and whether this varies by influencer type.Methods: We identified 988 of the most-followed Australian Instagram influencers and retrieved all 667,531 of their posts that were visible to all users, including those under 18 (i.e., not age gated). An LLM classified whether each post referenced alcohol and these were screened manually to record brand references. Influencers were coded into 14 types (e.g., athletes, actors).Results: Most influencers (87.45%) referenced alcohol and 47.10% referenced an alcohol brand in at least one post. Overall, 20,448 posts (3.06%) referenced alcohol and 2,869 (0.43%) a brand (14% of alcohol-related posts). Food influencers posted the most alcohol and brand-related content, while athletes posted the least alcohol-related content but had the highest proportion referencing a brand.Conclusions: Influencer alcohol-related and alcohol-related brand content is pervasive on Instagram and reaches audiences of every age, including those under 18 who should not see alcohol advertising.So what: Given the link between exposure and use, health promotion should aim policies and strategies at reducing alcohol-related content, particularly branded content that likely reflects influencer marketing.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al.· Zenodo (CERN European Organi...· 465 citations
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14