Zobrazeno 1 - 10
of 2 136
pro vyhledávání: '"Mancò, A."'
Large Language Models (LLMs) exhibit significant persuasion capabilities in one-on-one interactions, but their influence within social networks remains underexplored. This study investigates the potential social impact of LLMs in these environments,
Externí odkaz:
http://arxiv.org/abs/2411.13187
Autor:
Gallo, Daniela, Liguori, Angelica, Ritacco, Ettore, Caviglione, Luca, Durante, Fabrizio, Manco, Giuseppe
To achieve accurate and unbiased predictions, Machine Learning (ML) models rely on large, heterogeneous, and high-quality datasets. However, this could raise ethical and legal concerns regarding copyright and authorization aspects, especially when in
Externí odkaz:
http://arxiv.org/abs/2410.05819
Autor:
Coppolillo, Erica, Mungari, Simone, Ritacco, Ettore, Fabbri, Francesco, Minici, Marco, Bonchi, Francesco, Manco, Giuseppe
Digital platforms such as social media and e-commerce websites adopt Recommender Systems to provide value to the user. However, the social consequences deriving from their adoption are still unclear. Many scholars argue that recommenders may lead to
Externí odkaz:
http://arxiv.org/abs/2409.16478
Audio-text contrastive models have become a powerful approach in music representation learning. Despite their empirical success, however, little is known about the influence of key design choices on the quality of music-text representations learnt th
Externí odkaz:
http://arxiv.org/abs/2409.11498
Autor:
Ma, Yinghao, Øland, Anders, Ragni, Anton, Del Sette, Bleiz MacSen, Saitis, Charalampos, Donahue, Chris, Lin, Chenghua, Plachouras, Christos, Benetos, Emmanouil, Shatri, Elona, Morreale, Fabio, Zhang, Ge, Fazekas, György, Xia, Gus, Zhang, Huan, Manco, Ilaria, Huang, Jiawen, Guinot, Julien, Lin, Liwei, Marinelli, Luca, Lam, Max W. Y., Sharma, Megha, Kong, Qiuqiang, Dannenberg, Roger B., Yuan, Ruibin, Wu, Shangda, Wu, Shih-Lun, Dai, Shuqi, Lei, Shun, Kang, Shiyin, Dixon, Simon, Chen, Wenhu, Huang, Wenhao, Du, Xingjian, Qu, Xingwei, Tan, Xu, Li, Yizhi, Tian, Zeyue, Wu, Zhiyong, Wu, Zhizheng, Ma, Ziyang, Wang, Ziyu
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models
Externí odkaz:
http://arxiv.org/abs/2408.14340
Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-off, as higher diversity typically comes at the cost of relevance, resul
Externí odkaz:
http://arxiv.org/abs/2408.03772
Autor:
Weck, Benno, Manco, Ilaria, Benetos, Emmanouil, Quinton, Elio, Fazekas, George, Bogdanov, Dmitry
Multimodal models that jointly process audio and language hold great promise in audio understanding and are increasingly being adopted in the music domain. By allowing users to query via text and obtain information about a given audio input, these mo
Externí odkaz:
http://arxiv.org/abs/2408.01337
Recently, Large Language Models (LLMs) have showcased their potential in various natural language processing tasks, including code generation. However, while significant progress has been made in adapting LLMs to generate code for several imperative
Externí odkaz:
http://arxiv.org/abs/2407.18723
The scarcity of realistic datasets poses a significant challenge in benchmarking recommender systems and social network analysis methods and techniques. A common and effective solution is to generate synthetic data that simulates realistic interactio
Externí odkaz:
http://arxiv.org/abs/2407.16594
Signed Graph Neural Networks (SGNNs) have recently gained attention as an effective tool for several learning tasks on signed networks, i.e., graphs where edges have an associated polarity. One of these tasks is to predict the polarity of the links f
Externí odkaz:
http://arxiv.org/abs/2407.15643