Probing Neural Language Models for Human Tacit Assumptions
By Nathaniel Weir, Adam Poliak, Benjamin Van Durme. 2020
40 citations recorded by the source corpus.
Subfields: Large Language Models, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Large Language Models
- Yejin Choi
- Matt Gardner
- Cyprien de Masson d'Autume
- Phil Blunsom
- Anders Søgaard
- Artur Kulmizev
- Ellie Pavlick
- Robert L Logan IV
In Safety, Robustness & Interpretability
- Matt Gardner
- Yejin Choi
- Sameer Singh
- Phil Blunsom
- Anders Søgaard
- Artur Kulmizev
- Ellie Pavlick
- Vered Shwartz
Citations in the corpus
- Impact of Pretraining Term Frequencies on Few-Shot Reasoning (2022, background): “Prompting Prompting has been widely applied to study the factual (Petroni et al., 2019), commonsense (Davison et al., 2019; Weir et al., 2020; Lin et al., 2020), mathematical (Saxton et al., 2019), and other NLP task-related (Rad-ford et al., 2019; Shin et al., 2020) knowledge LMs acquire during…”
- Do Neural Language Models Overcome Reporting Bias? (2020, background): “Recently, Weir et al. (2020) studied LMs’ ability to associate concepts with their properties, by providing the LM the concept and predicting the properties and vice versa.”
- Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color (2021, background): “This includes, for instance, some ∗ For correspondence: {abdou,soegaard}@di.ku.dk features of concrete and abstract concepts, such as objects’ attributes and affordances (Forbes et al., 2019b; Weir et al., 2020).”
- A Systematic Investigation of Commonsense Understanding in Large Language Models (2021, background): “Previous work has probed for commonsense knowledge in large language models using knowledge base completion (Petroni et al., 2019; Davison et al., 2019) or manually designed probing tasks (Weir et al., 2020; Shwartz and Choi, 2020).”