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Status: Unchecked ×Keyword: reading comprehension ×Clear all

4 claims from 2 papers

1. Computer Science › Topic Modeling

   SQuAD: 100,000+ Questions for Machine Comprehension of Text

   Rajpurkar, Zhang, Lopyrev and Liang · arXiv (Cornell University) · 2016

   The paper introduces SQuAD, a dataset of over 100,000 crowdworker questions on Wikipedia articles, analyses the reasoning it needs, and reports a logistic regression model well below human performance.

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   1. UncheckedA logistic regression model scored 51.0% F1 on the SQuAD reading-comprehension dataset, against a simple baseline of 20%.“We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%).”
   2. UncheckedOn SQuAD, human performance (86.8%) is much higher than the authors' best logistic regression model (51.0% F1), which the authors say makes it a good challenge.“However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research.”

2. Computer Science › Topic Modeling

   Scaling Language Models: Methods, Analysis & Insights from Training Gopher

   Rae, Borgeaud, Cai et al. · arXiv (Cornell University) · 2021

   The paper analyses Transformer language models from tens of millions to 280 billion parameters (Gopher), covering performance across tasks, training data, bias and toxicity, and AI safety.

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   1. UncheckedMaking language models larger helps most with reading comprehension, fact-checking and spotting toxic language, and less with logical and mathematical reasoning.“Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit.”
   2. UncheckedLanguage models of many sizes, up to the 280-billion-parameter Gopher, were tested on 152 varied tasks and reached state-of-the-art results on most.“These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority.”