Zobrazeno 1 - 10
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pro vyhledávání: '"Nakov, P"'
Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. While recent efforts in robotics have leveraged LLMs both for high-level and low-level planning, thes
Externí odkaz:
http://arxiv.org/abs/2411.17636
Autor:
Ma, Congbo, Wang, Hu, Qiu, Zitai, Xue, Shan, Wu, Jia, Yang, Jian, Nakov, Preslav, Sheng, Quan Z.
Social media data is inherently rich, as it includes not only text content, but also users, geolocation, entities, temporal information, and their relationships. This data richness can be effectively modeled using heterogeneous information networks (
Externí odkaz:
http://arxiv.org/abs/2411.12588
The growing use of large language models (LLMs) has raised concerns regarding their safety. While many studies have focused on English, the safety of LLMs in Arabic, with its linguistic and cultural complexities, remains under-explored. Here, we aim
Externí odkaz:
http://arxiv.org/abs/2410.17040
Autor:
Suri, Manan, Mathur, Puneet, Dernoncourt, Franck, Jain, Rajiv, Morariu, Vlad I, Sawhney, Ramit, Nakov, Preslav, Manocha, Dinesh
Document structure editing involves manipulating localized textual, visual, and layout components in document images based on the user's requests. Past works have shown that multimodal grounding of user requests in the document image and identifying
Externí odkaz:
http://arxiv.org/abs/2410.16472
Autor:
Xie, Zhuohan, Xing, Rui, Wang, Yuxia, Geng, Jiahui, Iqbal, Hasan, Sahnan, Dhruv, Gurevych, Iryna, Nakov, Preslav
Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followe
Externí odkaz:
http://arxiv.org/abs/2411.00784
Autor:
Li, Haonan, Han, Xudong, Wang, Hao, Wang, Yuxia, Wang, Minghan, Xing, Rui, Geng, Yilin, Zhai, Zenan, Nakov, Preslav, Baldwin, Timothy
We introduce Loki, an open-source tool designed to address the growing problem of misinformation. Loki adopts a human-centered approach, striking a balance between the quality of fact-checking and the cost of human involvement. It decomposes the fact
Externí odkaz:
http://arxiv.org/abs/2410.01794
In this paper, we investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift, i.e., can LLMs generalize to domains that require specific knowledge such as medicine and law in a zero-shot fashion without addit
Externí odkaz:
http://arxiv.org/abs/2409.18446
Autor:
Shang, Guokan, Abdine, Hadi, Khoubrane, Yousef, Mohamed, Amr, Abbahaddou, Yassine, Ennadir, Sofiane, Momayiz, Imane, Ren, Xuguang, Moulines, Eric, Nakov, Preslav, Vazirgiannis, Michalis, Xing, Eric
We introduce Atlas-Chat, the first-ever collection of LLMs specifically developed for dialectal Arabic. Focusing on Moroccan Arabic, also known as Darija, we construct our instruction dataset by consolidating existing Darija language resources, creat
Externí odkaz:
http://arxiv.org/abs/2409.17912
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
Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning, which is crucial for tasks such as table question answering (TQA) and table-based fact verification (TFV). To addres
Externí odkaz:
http://arxiv.org/abs/2409.11724