13 resources

  • EdArXiv
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    Jun 2nd, 2023
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    report
    EdArXiv
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • EdArXiv
    |
    Jun 2nd, 2023
    |
    report
    EdArXiv
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • EdArXiv
    |
    Jun 2nd, 2023
    |
    report
    EdArXiv
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • Rose Wang, Dorottya Demszky
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    Jun 2nd, 2023
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    preprint
    Rose Wang, Dorottya Demszky
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • Zihao Zhou, Maizhen Ning, Qiufeng Wang
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    Apr 4th, 2023
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    conferencePaper
    Zihao Zhou, Maizhen Ning, Qiufeng Wang
    Apr 4th, 2023
  • Eric C. K. Cheng, Tianchong Wang, Tim Sc...
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    Apr 4th, 2023
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    book
    Eric C. K. Cheng, Tianchong Wang, Tim Sc...
    Apr 4th, 2023
  • Yang Liu, Dan Iter, Yichong Xu
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    May 23rd, 2023
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    preprint
    Yang Liu, Dan Iter, Yichong Xu
    May 23rd, 2023

    The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. Recent studies suggest using large language models (LLMs) as reference-free metrics for NLG evaluation, which have the benefit of being applicable to new tasks that lack human references....

  • Jiaan Wang, Yunlong Liang, Fandong Meng,...
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    Apr 4th, 2023
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    journalArticle
    Jiaan Wang, Yunlong Liang, Fandong Meng,...
    Apr 4th, 2023

    Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. Many prior studies have shown that ChatGPT achieves remarkable performance on various NLP tasks in terms of automatic evaluation metrics. However, the ability of ChatGPT to serve as an evaluation metric is still underexplored. Considering assessing the quality of natural language generation (NLG) models is an arduous task and NLG metrics notoriously show their poor correlation with...

  • Jason Wei, Xuezhi Wang, Dale Schuurmans,...
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    Jan 10th, 2023
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    preprint
    Jason Wei, Xuezhi Wang, Dale Schuurmans,...
    Jan 10th, 2023

    We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of...

  • Jacob Steiss, Tamara Tate, Steve Graham,...
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    Sep 7th, 2023
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    preprint
    Jacob Steiss, Tamara Tate, Steve Graham,...
    Sep 7th, 2023

    Offering students formative feedback on drafts of their writing is an effective way to facilitate writing development. This study examined the ability of generative AI (i.e., ChatGPT) to provide formative feedback on students’ compositions. We compared the quality of human and AI feedback by scoring the feedback each provided on secondary student essays (n=200) on five measures of feedback quality: the degree to which feedback (a) was criteria-based, (b) provided clear directions for...

  • Jacob Steiss, Tamara Tate, Steve Graham,...
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    Sep 7th, 2023
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    preprint
    Jacob Steiss, Tamara Tate, Steve Graham,...
    Sep 7th, 2023

    Offering students formative feedback on drafts of their writing is an effective way to facilitate writing development. This study examined the ability of generative AI (i.e., ChatGPT) to provide formative feedback on students’ compositions. We compared the quality of human and AI feedback by scoring the feedback each provided on secondary student essays (n=200) on five measures of feedback quality: the degree to which feedback (a) was criteria-based, (b) provided clear directions for...

  • Lei Huang, Weijiang Yu, Weitao Ma
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    Nov 9th, 2023
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    preprint
    Lei Huang, Weijiang Yu, Weitao Ma
    Nov 9th, 2023

    The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation. Nevertheless, alongside these strides, LLMs exhibit a critical tendency to produce hallucinations, resulting in content that is inconsistent with real-world facts or user inputs. This phenomenon poses substantial challenges to their practical deployment and raises concerns over the reliability of...

  • Rohan Anil, Andrew M. Dai, Orhan Firat
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    May 17th, 2023
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    preprint
    Rohan Anil, Andrew M. Dai, Orhan Firat
    May 17th, 2023

    We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more...

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