Zobrazeno 1 - 8
of 8
pro vyhledávání: '"de Langis, Karin"'
Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author's emotions or attitudes (e.g., disgust). An open question is how language models can be explicitly controlled so that they weave t
Externí odkaz:
http://arxiv.org/abs/2402.14146
Autor:
Das, Debarati, De Langis, Karin, Martin-Boyle, Anna, Kim, Jaehyung, Lee, Minhwa, Kim, Zae Myung, Hayati, Shirley Anugrah, Owan, Risako, Hu, Bin, Parkar, Ritik, Koo, Ryan, Park, Jonginn, Tyagi, Aahan, Ferland, Libby, Roy, Sanjali, Liu, Vincent, Kang, Dongyeop
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including annotations, preferences, instruction prompts, simulated dialogues, and f
Externí odkaz:
http://arxiv.org/abs/2401.14698
Capturing readers' engagement in fiction is a challenging but important aspect of narrative understanding. In this study, we collected 23 readers' reactions to 2 short stories through eye tracking, sentence-level annotations, and an overall engagemen
Externí odkaz:
http://arxiv.org/abs/2306.04043
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information
The success of NLP systems often relies on the availability of large, high-quality datasets. However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy. Several methods for characterizing datasets
Externí odkaz:
http://arxiv.org/abs/2305.19344
Autor:
de Langis, Karin, Kang, Dongyeop
There is growing interest in incorporating eye-tracking data and other implicit measures of human language processing into natural language processing (NLP) pipelines. The data from human language processing contain unique insight into human linguist
Externí odkaz:
http://arxiv.org/abs/2212.09873
Mobile robots in unstructured, mapless environments must rely on an obstacle avoidance module to navigate safely. The standard avoidance techniques estimate the locations of obstacles with respect to the robot but are unaware of the obstacles' identi
Externí odkaz:
http://arxiv.org/abs/2107.06401
With the end goal of selecting and using diver detection models to support human-robot collaboration capabilities such as diver following, we thoroughly analyze a large set of deep neural networks for diver detection. We begin by producing a dataset
Externí odkaz:
http://arxiv.org/abs/2012.05701
Autor:
de Langis, Karin, Sattar, Junaed
Autonomous underwater robots working with teams of human divers may need to distinguish between different divers, e.g. to recognize a lead diver or to follow a specific team member. This paper describes a technique that enables autonomous underwater
Externí odkaz:
http://arxiv.org/abs/1910.09636