Allen AI

JULY 7, 2023

2020) and Macaw (Tafjord and Clark, 2021), our results show that mental models derived using these LMs’ predictions are significantly inconsistent, with 19–43% conditional violation. Association for Computational Linguistics. Association for Computational Linguistics. Association for Computational Linguistics.

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Allen AI

JULY 8, 2023

HINT models outperform strong state-of-the-art baselines by over 10% when controlling for compute (measured in FLOPs). Submissions to EMNLP 2021 binned by count of YES responses to the NLP Reproducibility Checklist items. 2021) and summarization (Saunders et al., Read more on the blog here.

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Allen AI

MAY 30, 2023

2021), a recently proposed commonsense moral reasoning model, generates moral judgments for simple actions described in text. arXiv preprint arXiv:2110.07574 (2021). In Findings of the Association for Computational Linguistics: EMNLP 2020 , pp. If offered to a work colleague, it may even be viewed as a courteous gesture.

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Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP).   2021 saw the continuation of the development of ever larger pre-trained models. 2021 saw the development of alternative model architectures that are viable alternatives to the transformer. style loss.

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The Stanford AI Lab Blog

OCTOBER 13, 2021

Acknowledgements Thanks to the SAIL blog editors, Pang Wei Koh, and Shiori Sagawa for their helpful feedback on this blog post. This post is based off our ICLR 2021 paper : Selective Classification Can Magnify Disparities Across Groups. In Association for Computational Linguistics (ACL), pp.

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Mlearning.ai

FEBRUARY 6, 2023

For example, Stanford received around 55,471 applications in 2021 [5]. 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [7] 57th Annual Meeting of the Association for Computational Linguistics [9] C. 2022) Shtetl-Optimized: The Blog of Scott Aaronson. [11]

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The Stanford AI Lab Blog

JANUARY 21, 2022

In this blog post I’ll share some recent work that explores using data and supervision that can be easily collected through the web as a way of learning rewards for robots. 2021 with natural language descriptions for each video. So how can we scalably supervise the reward learning process? Chen, Suraj Nair, Chelsea Finn. Quillen, D.,

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