The task is to define whether they are used in the same sense or not. The schema takes its name from a well-known example by Terry Winograd. The set would then be presented as a challenge for AI programs like the Turing test. The strengths of the challenge are that it is clear-cut, in that the answer to each schema is a binary choice; vivid, in that it is evident to non-experts that a program that fails to get the correct answers has severe gaps in its understanding; and difficult, in that it is far beyond the current state of the art. A Winograd schema is a pair of sentences that differ in only one or two. The dataset will test the models’ ability to identify and resolve syntactic ambiguities using logic and knowledge about the world-the classic standard set by Terry Winograd. The dataset was first introduced in the Russian SuperGLUE benchmark, and it’s one of the sets for which there is still a significant gap between model and human estimates.

Let candidate be the fallback resource specified for the fallback namespace in question. If multiple application caches match, the user agent must use the fallback of the most appropriate application cache of those that match. If candidate is not marked as foreign, then the user agent must discard the failed load and instead continue along these steps using candidate as the resource. The document’s address, if appropriate, will still be the originally requested URL, not the fallback URL, but the user agent may indicate to the user that the original page load failed, that the page used was a fallback resource, and what the URL of the fallback resource actually is. 7. If the resource was not fetched from an application cache, and was to be fetched using HTTP GET or equivalent, and there are relevant application caches that are identified by a URL with the same origin as the URL in question, and that have this URL as one of their entries, excluding entries marked as foreign, and whose mode is prefer-online, and the user didn’t cancel the navigation attempt during the earlier step, and the navigation attempt failed (e.g.

↑ Fogie, Seth, Jeremiah Grossman, Robert Hansen, and Anton Rager. Cross Site Scripting Attacks: XSS Exploits and Defense (англ.). ↑ O’Reilly, Tim. What Is Web 2.0 (неопр.) 4-5. O’Reilly Media (30 сентября 2005). Дата обращения: 4 июня 2008. Архивировано 15 апреля 2013 года. ↑ Ritchie, Paul. The security risks of AJAX/web 2.0 applications (неопр.) // Infosecurity. Elsevier, 2007. — March. Архивировано 25 июня 2008 года. Архивированная копия (неопр.). Дата обращения: 15 апреля 2013. Архивировано из оригинала 25 июня 2008 года. ↑ Berinato, Scott (2007-01-01). «Software Vulnerability Disclosure: The Chilling Effect». CSO. CXO Media. p. ↑ Prince, Brian (2008-04-09). «McAfee Governance, Risk and Compliance Business Unit». WEEK. Ziff Davis Enterprise Holdings. ↑ Preston, Rob (2008-04-12). «Down To Business: It’s Past Time To Elevate The Infosec Conversation». InformationWeek. United Business Media. ↑ Claburn, Thomas (2007-02-06). «RSA’s Coviello Predicts Security Consolidation». InformationWeek. United Business Media. ↑ boyd, danah; Hargittai, Eszter. Facebook privacy settings: Who cares? First Monday. — University of Illinois at Chicago, 2010. — July (т. 15, № 8). Архивировано 4 февраля 2011 года.

Each subtask contains a parentheses sequence. The model’s goal is to correctly predict whether the sequence is balanced. 1. Open brackets must be closed by the same type of brackets. 2. Open brackets must be closed in the correct order. 3. Every close bracket has a corresponding open bracket of the same type. Algorithms are a way to extrapolate examples and are some of the most concise descriptions of a pattern. In that sense, the ability of language models to learn them is a prominent measure of intelligence. The train consists of 250 examples, and the test set includes 1000 examples. 8 prompts of varying difficulty were created for this task. The task is evaluated using Accuracy. The human benchmark is measured on a subset of size 100 (sampled with the same original distribution). The task contains questions from the game “What? The dataset consists of 29,376 training examples (train set) and 416 test examples (test set). We prepared 4 different prompts of various difficulties for this task.