Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Jurgens, David"'
Podcasts provide highly diverse content to a massive listener base through a unique on-demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset
Externí odkaz:
http://arxiv.org/abs/2411.07892
The Internet has significantly expanded the potential for global collaboration, allowing millions of users to contribute to collective projects like Wikipedia. While prior work has assessed the success of online collaborations, most approaches are ti
Externí odkaz:
http://arxiv.org/abs/2410.19150
Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on optimizing prompts specific to a task. However, much less attention has b
Externí odkaz:
http://arxiv.org/abs/2410.14826
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue
Autor:
Ivey, Jonathan, Kumar, Shivani, Liu, Jiayu, Shen, Hua, Rakshit, Sushrita, Raju, Rohan, Zhang, Haotian, Ananthasubramaniam, Aparna, Kim, Junghwan, Yi, Bowen, Wright, Dustin, Israeli, Abraham, Møller, Anders Giovanni, Zhang, Lechen, Jurgens, David
Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, much recent work has sought to use large language models (LLMs) to simula
Externí odkaz:
http://arxiv.org/abs/2409.08330
Psychological trauma can manifest following various distressing events and is captured in diverse online contexts. However, studies traditionally focus on a single aspect of trauma, often neglecting the transferability of findings across different sc
Externí odkaz:
http://arxiv.org/abs/2408.05977
Although the spread of behaviors is influenced by many social factors, existing literature tends to study the effects of single factors -- most often, properties of the social network -- on the final cascade. In order to move towards a more integrate
Externí odkaz:
http://arxiv.org/abs/2407.12771
Autor:
Park, Chan Young, Li, Shuyue Stella, Jung, Hayoung, Volkova, Svitlana, Mitra, Tanushree, Jurgens, David, Tsvetkov, Yulia
This study introduces ValueScope, a framework leveraging language models to quantify social norms and values within online communities, grounded in social science perspectives on normative structures. We employ ValueScope to dissect and analyze lingu
Externí odkaz:
http://arxiv.org/abs/2407.02472
Autor:
Shen, Hua, Knearem, Tiffany, Ghosh, Reshmi, Alkiek, Kenan, Krishna, Kundan, Liu, Yachuan, Ma, Ziqiao, Petridis, Savvas, Peng, Yi-Hao, Qiwei, Li, Rakshit, Sushrita, Si, Chenglei, Xie, Yutong, Bigham, Jeffrey P., Bentley, Frank, Chai, Joyce, Lipton, Zachary, Mei, Qiaozhu, Mihalcea, Rada, Terry, Michael, Yang, Diyi, Morris, Meredith Ringel, Resnick, Paul, Jurgens, David
Recent advancements in general-purpose AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. However, the lack of clar
Externí odkaz:
http://arxiv.org/abs/2406.09264
Understanding the writing frame of news articles is vital for addressing social issues, and thus has attracted notable attention in the fields of communication studies. Yet, assessing such news article frames remains a challenge due to the absence of
Externí odkaz:
http://arxiv.org/abs/2405.13272
Language technologies have made enormous progress, especially with the introduction of large language models (LLMs). On traditional tasks such as machine translation and sentiment analysis, these models perform at near-human level. These advances can
Externí odkaz:
http://arxiv.org/abs/2405.02411