Search
702 resources
-
Sami Baral, Anthony F. Botelho, John A. ...|Oct 27th, 2021|conferencePaperSami Baral, Anthony F. Botelho, John A. ...Oct 27th, 2021
-
Randy Elliot Bennett, Mo Zhang, Sandip S...|Oct 27th, 2021|journalArticleRandy Elliot Bennett, Mo Zhang, Sandip S...Oct 27th, 2021
This study examined differences in the composition processes used by educationally at-risk males and females who wrote essays as part of a high-school equivalency examination. Over 30,000 individuals were assessed, each taking one of 12 forms of the examination’s language arts writing subtest in 23 US states. Writing processes were inferred using features extracted from keystroke logs and aggregated into seven composite indicators. Results showed that females earned higher essay and total...
-
Guembe Blessing, Ambrose Azeta, Sanjay M...|Oct 27th, 2021|bookSectionGuembe Blessing, Ambrose Azeta, Sanjay M...Oct 27th, 2021
-
Rishi Bommasani, Drew A. Hudson, Ehsan A...|Oct 27th, 2021|journalArticleRishi Bommasani, Drew A. Hudson, Ehsan A...Oct 27th, 2021
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical...
-
Elizabeth Clark, Tal August, Sofia Serra...|Oct 27th, 2021|preprintElizabeth Clark, Tal August, Sofia Serra...Oct 27th, 2021
Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts' ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore...
-
Elizabeth Clark, Tal August, Sofia Serra...|Oct 27th, 2021|preprintElizabeth Clark, Tal August, Sofia Serra...Oct 27th, 2021
Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts' ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore...
-
Saad Khan, Jesse Hamer, Tiago Almeida|Oct 27th, 2021|conferencePaperSaad Khan, Jesse Hamer, Tiago AlmeidaOct 27th, 2021
We present Generate, a AI-human hybrid system to help education content creators interactively generate assessment content in an efficient and scalable manner. Our system integrates advanced natural language generation (NLG) approaches with subject matter expertise of assessment developers to efficiently generate a large number of highly customized and valid assessment items. We utilize the powerful Transformer architecture which is capable of leveraging substantive pretraining on several...
-
Vivekanandan S. Kumar, David Boulanger|Sep 27th, 2021|journalArticleVivekanandan S. Kumar, David BoulangerSep 27th, 2021
-
Michael McTear, Michael McTear|Oct 27th, 2021|bookSectionMichael McTear, Michael McTearOct 27th, 2021
-
Gabriel Oliveira dos Santos, Esther Luna...|Oct 27th, 2021|preprintGabriel Oliveira dos Santos, Esther Luna...Oct 27th, 2021
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset. We also show that CIDEr-D has performance hampered by the lack of multiple reference sentences and high variance of sentence length. To bypass this problem, we introduce CIDEr-R, which improves CIDEr-D, making it more flexible in dealing with datasets with high...
-
University of Wolverhampton, UK, Hadeel ...|Oct 27th, 2021|conferencePaperUniversity of Wolverhampton, UK, Hadeel ...Oct 27th, 2021
-
Masaki Uto|Jul 27th, 2021|journalArticleMasaki UtoJul 27th, 2021
Abstract Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by humans. Although traditional AES models typically rely on manually designed features, deep neural network (DNN)-based AES models that obviate the need for feature engineering have recently attracted increased attention. Various DNN-AES models with different characteristics have been proposed over the past few years. To our knowledge, however, no study has...
-
Cong Wang, Xiufeng Liu, Lei Wang|Apr 27th, 2021|journalArticleCong Wang, Xiufeng Liu, Lei WangApr 27th, 2021
-
Tianqi Wang, Hiroaki Funayama, Hiroki Ou...|Oct 27th, 2021|journalArticleTianqi Wang, Hiroaki Funayama, Hiroki Ou...Oct 27th, 2021
-
Zichao Wang, Andrew Lan, Richard Baraniu...|Oct 27th, 2021|conferencePaperZichao Wang, Andrew Lan, Richard Baraniu...Oct 27th, 2021
-
Kim Christopher Williamson, René F. Kizi...|Oct 27th, 2021|conferencePaperKim Christopher Williamson, René F. Kizi...Oct 27th, 2021
-
Mike Wu, Noah Goodman, Chris Piech|Oct 27th, 2021|journalArticleMike Wu, Noah Goodman, Chris PiechOct 27th, 2021
High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale. While this feedback could in principle be automated, supervised approaches to predicting the correct feedback are bottlenecked by the intractability of annotating large quantities of student code. In this paper, we instead frame the problem of providing feedback as few-shot classification, where a meta-learner adapts to give feedback to student code on a new programming...
-
Khensani Xivuri, Hossana Twinomurinzi, D...|Oct 27th, 2021|bookSectionKhensani Xivuri, Hossana Twinomurinzi, D...Oct 27th, 2021
-
Jing Xu, Da Ju, Margaret Li|Oct 27th, 2021|conferencePaperJing Xu, Da Ju, Margaret LiOct 27th, 2021
-
Goh Ying Yingsoon, Niaz Chowdhury, Ganes...|Oct 27th, 2021|bookSectionGoh Ying Yingsoon, Niaz Chowdhury, Ganes...Oct 27th, 2021
The teaching of Chinese as a foreign language can be supported by using AI technology. Traditionally, the non-native learners can only interact with the instructors and depend on them solely for speaking practices. However, with the advancement of AI technology, the learners can use AI technology for interactive speaking skill development. In this study, the learners were instructed to download an application at https://m.wandoujia.com/apps/6790950. The process on the preparation of this AI...