Zobrazeno 1 - 10
of 406
pro vyhledávání: '"Barbieri, Francesco"'
In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this content across various languages remain an important challenge with traditi
Externí odkaz:
http://arxiv.org/abs/2410.03075
Autor:
Calabrese, Agostina, Neves, Leonardo, Shah, Neil, Bos, Maarten W., Ross, Björn, Lapata, Mirella, Barbieri, Francesco
Content moderators play a key role in keeping the conversation on social media healthy. While the high volume of content they need to judge represents a bottleneck to the moderation pipeline, no studies have explored how models could support them to
Externí odkaz:
http://arxiv.org/abs/2406.04106
Autor:
Zhou, Zhihan, Fang, Qixiang, Neves, Leonardo, Barbieri, Francesco, Liu, Yozen, Liu, Han, Bos, Maarten W., Dotsch, Ron
User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings from behavioral data. Yet behavior-based user embedding learning face
Externí odkaz:
http://arxiv.org/abs/2403.13344
Autor:
Maharana, Adyasha, Lee, Dong-Ho, Tulyakov, Sergey, Bansal, Mohit, Barbieri, Francesco, Fang, Yuwei
Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models (LLMs) and retrieval augmented generation (RAG)
Externí odkaz:
http://arxiv.org/abs/2402.17753
Autor:
Fang, Qixiang, Zhou, Zhihan, Barbieri, Francesco, Liu, Yozen, Neves, Leonardo, Nguyen, Dong, Oberski, Daniel L., Bos, Maarten W., Dotsch, Ron
Learning general-purpose user representations based on user behavioral logs is an increasingly popular user modeling approach. It benefits from easily available, privacy-friendly yet expressive data, and does not require extensive re-tuning of the up
Externí odkaz:
http://arxiv.org/abs/2312.12111
Autor:
Zhang, Zhihan, Lee, Dong-Ho, Fang, Yuwei, Yu, Wenhao, Jia, Mengzhao, Jiang, Meng, Barbieri, Francesco
Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-resource languages, its application in lower-resource ones faces challenges due to the
Externí odkaz:
http://arxiv.org/abs/2311.08711
Autor:
Antypas, Dimosthenis, Ushio, Asahi, Barbieri, Francesco, Neves, Leonardo, Rezaee, Kiamehr, Espinosa-Anke, Luis, Pei, Jiaxin, Camacho-Collados, Jose
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the community to know, for instance, given a task, which is the best perf
Externí odkaz:
http://arxiv.org/abs/2310.14757
Autor:
Peters, Heinrich, Liu, Yozen, Barbieri, Francesco, Baten, Raiyan Abdul, Matz, Sandra C., Bos, Maarten W.
The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-pres
Externí odkaz:
http://arxiv.org/abs/2310.14533
Autor:
Loureiro, Daniel, Rezaee, Kiamehr, Riahi, Talayeh, Barbieri, Francesco, Neves, Leonardo, Anke, Luis Espinosa, Camacho-Collados, Jose
This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models. This data comprises the past five years and captures changes in n-gra
Externí odkaz:
http://arxiv.org/abs/2308.02142
Autor:
Jiang, Julie, Dotsch, Ron, Roura, Mireia Triguero, Liu, Yozen, Silva, Vítor, Bos, Maarten W., Barbieri, Francesco
Publikováno v:
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Pictorial emojis and stickers are commonly used in online social networking to facilitate and aid communications. We delve into the use of Bitmoji stickers, a highly expressive form of pictorial communication using avatars resembling actual users. We
Externí odkaz:
http://arxiv.org/abs/2211.01426