Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Lertvittayakumjorn, Piyawat"'
Capacitive touch sensors capture the two-dimensional spatial profile (referred to as a touch heatmap) of a finger's contact with a mobile touchscreen. However, the research and design of touchscreen mobile keyboards -- one of the most speed and accur
Externí odkaz:
http://arxiv.org/abs/2410.02264
Autor:
Lin, Susan, Warner, Jeremy, Zamfirescu-Pereira, J. D., Lee, Matthew G., Jain, Sauhard, Huang, Michael Xuelin, Lertvittayakumjorn, Piyawat, Cai, Shanqing, Zhai, Shumin, Hartmann, Björn, Liu, Can
Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface tha
Externí odkaz:
http://arxiv.org/abs/2401.10838
Prompt-based learning has shown its effectiveness in few-shot text classification. One important factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledge
Externí odkaz:
http://arxiv.org/abs/2310.12778
Autor:
Leiter, Christoph, Lertvittayakumjorn, Piyawat, Fomicheva, Marina, Zhao, Wei, Gao, Yang, Eger, Steffen
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics for machine translation (for example, COMET or BERTScore) are based on black-box large language models. They often achieve strong correlations with human judgments
Externí odkaz:
http://arxiv.org/abs/2306.13041
Recent works in Explainable AI mostly address the transparency issue of black-box models or create explanations for any kind of models (i.e., they are model-agnostic), while leaving explanations of interpretable models largely underexplored. In this
Externí odkaz:
http://arxiv.org/abs/2205.10932
Autor:
Leiter, Christoph, Lertvittayakumjorn, Piyawat, Fomicheva, Marina, Zhao, Wei, Gao, Yang, Eger, Steffen
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics (such as BERTScore or MoverScore) are based on black-box language models such as BERT or XLM-R. They often achieve strong correlations with human judgments, but re
Externí odkaz:
http://arxiv.org/abs/2203.11131
In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentence-level score indicating the overall quality of the translation, but a
Externí odkaz:
http://arxiv.org/abs/2110.04392
Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we
Externí odkaz:
http://arxiv.org/abs/2104.15135
Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available Python library for extracting patterns from textua
Externí odkaz:
http://arxiv.org/abs/2104.03958
Despite the recent, widespread focus on eXplainable AI (XAI), explanations computed by XAI methods tend to provide little insight into the functioning of Neural Networks (NNs). We propose a novel framework for obtaining (local) explanations from NNs
Externí odkaz:
http://arxiv.org/abs/2012.05766