Your submission is now in Draft mode.

Once it's ready, please submit your draft for review by our team of Community Moderators. Thank you!

Submit Essay

Once you submit your essay, you can no longer edit it.


This content now needs to be approved by community moderators.


This essay was submitted and is waiting for review.

Date when AI outperforms humans on reasoning


Recent natural language processing (NLP) models have succeeded in generating human-level text and translations. However questions remain regarding to what extent this success relies on understanding, as opposed to memorization of statistical patterns.

A recent paper showed that when statistical-cues are removed, state of the art NLP models fail on argument reasoning tasks -- despite human performance remaining unaffected. Untrained humans perform at ~80% accuracy on this argument reasoning task, whereas recent NLP models perform near 50%.

When will a machine learning model out-perform the human-level of 80% accuracy on this benchmark? This question resolves when either:

  1. A paper posted on claims a greater than 80% accuracy on the Niven and Kao benchmark.
  2. A paper posted on claims a greater than 80% accuracy on a successor* dataset to the Niven and Kao data.

*A successor dataset will count towards this resolution criterion if it satisfies all of the following:

  1. Published in an pre-print intended to quantify argument and/or reasoning

  2. Cites Niven and Kao

  3. Pre-2020 NLP models show random-level performance on the dataset (<=60% accuracy for a binary task, <=100*(1/n+1/n/5)% for an n-ary task)

If the successor dataset includes information on human-level performance, that threshold will be used instead of the 80% accuracy threshold.

Make a Prediction


Note: this question resolved before its original close time. All of your predictions came after the resolution, so you did not gain (or lose) any points for it.

Note: this question resolved before its original close time. You earned points up until the question resolution, but not afterwards.

Current points depend on your prediction, the community's prediction, and the result. Your total earned points are averaged over the lifetime of the question, so predict early to get as many points as possible! See the FAQ.