Policy Studies Organization

Nagel, Stuart S.

doi:10.1177/109821408100200112

Cited by 3 publications

(1 citation statement)

References 88 publications

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“…Table 3: Summary of the 22 models that we evaluate in this paper. Legend for Corpora: WIKI: Wikipedia; BC: BookCorpus (Zhu et al, 2015); CW: ClueWeb (Callan et al, 2009); CC: CommonCrawl GIGA: Gigaword (Graff et al, 2003); OWTC: OpenWebTextCorpus (Gokaslan and Cohen, 2019); CC-NEWS: CommonCrawl News (Nagel, 2016); STORIES: Stories corpus (Trinh and Le, 2018); WEBTEXT: WebText corpus (Radford et al, 2019); PILE:…”

Section: Additional Findings and Analysesmentioning

confidence: 99%

COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models

Misra,

Rayz,

Ettinger

2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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A characteristic feature of human semantic cognition is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties (can breathe) from superordinate concepts (ANIMAL) to their subordinates (DOG)-i.e. demonstrate property inheritance.In this paper, we present COMPS, a collection of English minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLMs on COMPS reveal that they can easily distinguish between concepts on the basis of a property when they are trivially different, but find it relatively difficult when concepts are related on the basis of nuanced knowledge representations. Furthermore, we find that PLMs can show behaviors suggesting successful property inheritance in simple contexts, but fail in the presence of distracting information, which decreases the performance of many models sometimes even below chance. This lack of robustness in demonstrating simple reasoning raises important questions about PLMs' capacity to make correct inferences even when they appear to possess the prerequisite knowledge.

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Section: Additional Findings and Analysesmentioning

confidence: 99%

COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models

Misra,

Rayz,

Ettinger

2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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Treatment of Aptitude × Treatment Interactions.

Smith

Sechrest²

1991

Journal of Consulting and Clinical Psychology

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Encouraging and Evaluating Class Participation

Czekanski¹,

Wolf²

2013

JUTLP

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Many faculty interpret student responses to faculty questions as evidence of an actively engaged classroom. Because of this conviction, class participation, whether graded or ungraded, appears in many course syllabi in colleges and universities and is often promoted as the responsibility of students to contribute to the learning environment. Class participation provides faculty with some confidence that learning is taking place during a course and that students are reading assignments. While faculty may debate that attendance should not be used as a stand-in for class participation, this may not be a universally held belief or practice. Some faculty create rubrics which structure student participation and often delineate the tasks students should perform. Rubrics often list points, percents, and scales indicating levels of performance. This scoping review of the literature identifies themes encouraging and grading class participation and provides examples to increase class participation.

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Policy Studies Organization

Cited by 3 publications

References 88 publications

COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models

COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models

Treatment of Aptitude × Treatment Interactions.

Encouraging and Evaluating Class Participation

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