Zobrazeno 1 - 10
of 11 204
pro vyhledávání: '"Hirschberg A."'
Autor:
Hui, Zheng, Guo, Zhaoxiao, Zhao, Hang, Duan, Juanyong, Ai, Lin, Li, Yinheng, Hirschberg, Julia, Huang, Congrui
Effective toxic content detection relies heavily on high-quality and diverse data, which serves as the foundation for robust content moderation models. This study explores the potential of open-source LLMs for harmful data synthesis, utilizing prompt
Externí odkaz:
http://arxiv.org/abs/2411.15175
Users can divulge sensitive information to proprietary LLM providers, raising significant privacy concerns. While open-source models, hosted locally on the user's machine, alleviate some concerns, models that users can host locally are often less cap
Externí odkaz:
http://arxiv.org/abs/2410.17127
Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. Our study systematically analyzes self-anthropomorphic expression within various dialogue d
Externí odkaz:
http://arxiv.org/abs/2410.03870
While recent advances in Text-to-Speech (TTS) technology produce natural and expressive speech, they lack the option for users to select emotion and control intensity. We propose EmoKnob, a framework that allows fine-grained emotion control in speech
Externí odkaz:
http://arxiv.org/abs/2410.00316
Autor:
Liu, Jiateng, Ai, Lin, Liu, Zizhou, Karisani, Payam, Hui, Zheng, Fung, May, Nakov, Preslav, Hirschberg, Julia, Ji, Heng
Propaganda plays a critical role in shaping public opinion and fueling disinformation. While existing research primarily focuses on identifying propaganda techniques, it lacks the ability to capture the broader motives and the impacts of such content
Externí odkaz:
http://arxiv.org/abs/2409.18997
Autor:
Gong, Ziwei, Ai, Lin, Deshpande, Harshsaiprasad, Johnson, Alexander, Phung, Emmy, Wu, Zehui, Emami, Ahmad, Hirschberg, Julia
Large Language Models (LLMs) have spurred interest in automatic evaluation methods for summarization, offering a faster, more cost-effective alternative to human evaluation. However, existing methods often fall short when applied to complex tasks lik
Externí odkaz:
http://arxiv.org/abs/2409.10883
Autor:
Ai, Lin, Gong, Ziwei, Deshpande, Harshsaiprasad, Johnson, Alexander, Phung, Emmy, Emami, Ahmad, Hirschberg, Julia
The rapid expansion of online content has intensified the issue of information redundancy, underscoring the need for solutions that can identify genuinely new information. Despite this challenge, the research community has seen a decline in focus on
Externí odkaz:
http://arxiv.org/abs/2409.09249
Emotion recognition in speech is a challenging multimodal task that requires understanding both verbal content and vocal nuances. This paper introduces a novel approach to emotion detection using Large Language Models (LLMs), which have demonstrated
Externí odkaz:
http://arxiv.org/abs/2407.21315
Dialogue systems have been used as conversation partners in English learning, but few have studied whether these systems improve learning outcomes. Student passion and perseverance, or grit, has been associated with language learning success. Recent
Externí odkaz:
http://arxiv.org/abs/2406.17982
Autor:
Ai, Lin, Kumarage, Tharindu, Bhattacharjee, Amrita, Liu, Zizhou, Hui, Zheng, Davinroy, Michael, Cook, James, Cassani, Laura, Trapeznikov, Kirill, Kirchner, Matthias, Basharat, Arslan, Hoogs, Anthony, Garland, Joshua, Liu, Huan, Hirschberg, Julia
The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks. This study investi
Externí odkaz:
http://arxiv.org/abs/2406.12263