A comprehensive study of vector leptoquark with U(1)B3−L2U(1)_{B_3-L_2} on the BB-meson and Muon g-2 anomalies

Recently reported anomalies in various $B$ meson decays and also in the anomalous magnetic moment of muon $(g-2)_\mu$ motivate us to consider a particular extension of the standard model incorporating new interactions in lepton and quark sectors simultaneously. Our minimal choice would be leptoquark. In particular, we take vector leptoquark ($U_1$) and comprehensively study all related observables including ${(g-2)_{\mu}},\ R_{K^{(*)}},\ R_{D^{(*)}}$,$B \to (K) \ell \ell' $where$\ell\ell'$are various combinations of$\mu$and$\tau$, and also lepton flavor violation in the$\tau$decays. We find that a hybrid scenario with additional$U(1)_{B_3-L_2}$ gauge boson provides a common explanation of all these anomalies.Comment: 16 pages, 3 figure

KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application

Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data. This poses a critical risk when deploying LLM-based applications. Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. This limitation requires localized social bias datasets to ensure the safe and effective deployment of LLMs. To this end, we present KO SB I, a new social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. We find that through filtering-based moderation, social biases in generated content can be reduced by 16.47%p on average for HyperCLOVA (30B and 82B), and GPT-3.Comment: 17 pages, 8 figures, 12 tables, ACL 202

SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration

The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with this concern while interacting with ill-intentioned users, such as those who explicitly make hate speech or elicit harmful responses. However, discussions on sensitive issues can become toxic even if the users are well-intentioned. For safer models in such scenarios, we present the Sensitive Questions and Acceptable Response (SQuARe) dataset, a large-scale Korean dataset of 49k sensitive questions with 42k acceptable and 46k non-acceptable responses. The dataset was constructed leveraging HyperCLOVA in a human-in-the-loop manner based on real news headlines. Experiments show that acceptable response generation significantly improves for HyperCLOVA and GPT-3, demonstrating the efficacy of this dataset.Comment: 19 pages, 10 figures, ACL 202

Enzyme-Triggered Depolymerization of Polymeric Micelles for Targeted Anticancer Therapy

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Enzyme-responsive Polymeric Micelles by Controlled Depolymerization for Anticancer Drug Delivery

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Disassembly of Polymeric Micelles by Enzyme-Triggered Depolymerization for Targeted Tumor Therapy

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