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--- nlp:datasets [2022/03/02 19:33] – [Multi-Task] jmflanig
+++ nlp:datasets [2023/11/29 21:14] (current) – jmflanig
@@ Line 1: / Line 1: @@
 ====== NLP Datasets ======
-See also [[http://nlpprogress.com/|NLP Progress]] and [[https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research#Text_data|Wikipedia List of datasets]].  Also [[data preparation]].
+See also [[http://nlpprogress.com/|NLP Progress]], [[https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research#Text_data|Wikipedia List of datasets]], and [[https://github.com/niderhoff/nlp-datasets|nlp-datasets]].  Also [[data preparation]].
 ===== Language Modeling Corpora =====
@@ Line 8: / Line 8: @@
   * [[https://arxiv.org/pdf/1506.06724.pdf|Bookcorpus]] (Used in BERT)
-===== Multi-Task =====
+===== General Benchmarks or Multi-Task Benchmarks =====
-  * [[https://gluebenchmark.com/|GLUE]]: [[https://arxiv.org/pdf/1804.07461.pdf|paper]]. Warning: Has an issue with QQP and WNLI due to dev and tests sets not coming from the same distribution. See FAQ [[https://gluebenchmark.com/faq/|here]].
+  * [[https://gluebenchmark.com/|GLUE]]: [[https://arxiv.org/pdf/1804.07461.pdf|paper]] - Warning: Has an issue with QQP and WNLI due to dev and tests sets not coming from the same distribution. See FAQ [[https://gluebenchmark.com/faq/|here]].
-  * [[https://super.gluebenchmark.com/|SuperGLUE]]: [[https://arxiv.org/pdf/1905.00537.pdf|paper]]. A more difficult version of GLUE.
+  * [[https://super.gluebenchmark.com/|SuperGLUE]]: [[https://arxiv.org/pdf/1905.00537.pdf|paper]] - A more difficult version of GLUE.
+  * [[https://www.cluebenchmarks.com/en/index.html|CLUE]]: [[https://arxiv.org/pdf/2004.05986.pdf|paper]] - Like GLUE, but for Chinese
+  * [[https://github.com/hendrycks/test|MMMLU]]: [[https://arxiv.org/pdf/2009.03300.pdf|Hendrycks et al 2020 - Measuring Massive Multitask Language Understanding]] This dataset is a popular dataset for LLMs to evaluate on (for example GPT-4, etc).  However, it has two serious issues. 1) the test set is available on the web, which means LLMs are likely contaminated, and 2) the datasets has no in-domain training data, and can only be evaluated in a few-shot manner. This make is impossible to properly compare to prior fine-tuned methods.
+===== Multilingual =====
+  * Survey on Multilingual NLP Datasets: [[https://multilingual-dataset-survey.github.io/full-survey/|List of Datasets]] and [[https://arxiv.org/pdf/2211.15649.pdf|Paper]]
 ===== Dialog =====