Zobrazeno 1 - 10
of 799
pro vyhledávání: '"Nenkova, A."'
Autor:
Cao, Shengcao, Gu, Jiuxiang, Kuen, Jason, Tan, Hao, Zhang, Ruiyi, Zhao, Handong, Nenkova, Ani, Gui, Liang-Yan, Sun, Tong, Wang, Yu-Xiong
Open-world entity segmentation, as an emerging computer vision task, aims at segmenting entities in images without being restricted by pre-defined classes, offering impressive generalization capabilities on unseen images and concepts. Despite its pro
Externí odkaz:
http://arxiv.org/abs/2404.12386
The diversity across outputs generated by large language models shapes the perception of their quality and utility. Prompt leaks, templated answer structure, and canned responses across different interactions are readily noticed by people, but there
Externí odkaz:
http://arxiv.org/abs/2403.00553
Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace. In practice one may now wish to choose confidently, but with minimal effort, the best perform
Externí odkaz:
http://arxiv.org/abs/2402.18756
Autor:
Chu, Zhendong, Zhang, Ruiyi, Yu, Tong, Jain, Rajiv, Morariu, Vlad I, Gu, Jiuxiang, Nenkova, Ani
To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumulate. In contrast, real-world applications often resort to massive low-
Externí odkaz:
http://arxiv.org/abs/2310.16790
Autor:
Zhu, Sicheng, Zhang, Ruiyi, An, Bang, Wu, Gang, Barrow, Joe, Wang, Zichao, Huang, Furong, Nenkova, Ani, Sun, Tong
Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks. Recent studies suggest that defending against these attacks is possible: adversarial attacks generate unlimited but
Externí odkaz:
http://arxiv.org/abs/2310.15140
Autor:
Saad-Falcon, Jon, Barrow, Joe, Siu, Alexa, Nenkova, Ani, Yoon, David Seunghyun, Rossi, Ryan A., Dernoncourt, Franck
Large Language Models (LLMs) have issues with document question answering (QA) in situations where the document is unable to fit in the small context length of an LLM. To overcome this issue, most existing works focus on retrieving the relevant conte
Externí odkaz:
http://arxiv.org/abs/2309.08872
Autor:
Demeter, David, Agarwal, Oshin, Igeri, Simon Ben, Sterbentz, Marko, Molino, Neil, Conroy, John M., Nenkova, Ani
Academic literature does not give much guidance on how to build the best possible customer-facing summarization system from existing research components. Here we present analyses to inform the selection of a system backbone from popular models; we fi
Externí odkaz:
http://arxiv.org/abs/2306.10555
Autor:
Xie, Kaige, Yu, Tong, Wang, Haoliang, Wu, Junda, Zhao, Handong, Zhang, Ruiyi, Mahadik, Kanak, Nenkova, Ani, Riedl, Mark
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To efficiently learn from few-shot samples, previous works have utilized massiv
Externí odkaz:
http://arxiv.org/abs/2305.12077
Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and generation models to augment text with relevant images. This is particul
Externí odkaz:
http://arxiv.org/abs/2305.10434
Autor:
Huang, Chieh-Yang, Hsu, Ting-Yao, Rossi, Ryan, Nenkova, Ani, Kim, Sungchul, Chan, Gromit Yeuk-Yin, Koh, Eunyee, Giles, Clyde Lee, Huang, Ting-Hao 'Kenneth'
Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation could aid paper writers by providing good starting captions that can
Externí odkaz:
http://arxiv.org/abs/2302.12324