Search Results for author:
Found 14 papers, 7 papers with code
Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs
Training on a corpus of human-scale in size (100M words), we iteratively trained transformer language models on systematically manipulated corpora and then evaluated their learning of a particular rare grammatical phenomenon: the English Article+Adjective+Numeral+Noun (AANN) construction (``a beautiful five days'').
Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently
Recent zero-shot evaluations have highlighted important limitations in the abilities of language models (LMs) to perform meaning extraction.
Abstraction via exemplars? A representational case study on lexical category inference in BERT
Exemplar based accounts are often considered to be in direct opposition to pure linguistic abstraction in explaining language learners' ability to generalize to novel expressions.
Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks
Despite readily memorizing world knowledge about entities, pre-trained language models (LMs) struggle to compose together two or more facts to perform multi-hop reasoning in question-answering tasks.
Large Language Models Can Be Easily Distracted by Irrelevant Context
We use this benchmark to measure the distractibility of cutting-edge prompting techniques for large language models, and find that the model performance is dramatically decreased when irrelevant information is included.
Language model acceptability judgements are not always robust to context
In this paper, we investigate the stability of language models' performance on targeted syntactic evaluations as we vary properties of the input context: the length of the context, the types of syntactic phenomena it contains, and whether or not there are violations of grammaticality.
COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models
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.
A Property Induction Framework for Neural Language Models
To what extent can experience from language contribute to our conceptual knowledge?
minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models
We present minicons, an open source library that provides a standard API for researchers interested in conducting behavioral and representational analyses of transformer-based language models (LMs).
On Semantic Cognition, Inductive Generalization, and Language Models
My doctoral research focuses on understanding semantic knowledge in neural network models trained solely to predict natural language (referred to as language models, or LMs), by drawing on insights from the study of concepts and categories grounded in cognitive science.
Do language models learn typicality judgments from text?
Building on research arguing for the possibility of conceptual and categorical knowledge acquisition through statistics contained in language, we evaluate predictive language models (LMs) -- informed solely by textual input -- on a prevalent phenomenon in cognitive science: typicality.
Finding Fuzziness in Neural Network Models of Language Processing
Humans often communicate by using imprecise language, suggesting that fuzzy concepts with unclear boundaries are prevalent in language use.
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference
Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences (premise and hypothesis).
Natural Language Inference
Natural Language Understanding
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Exploring BERT's Sensitivity to Lexical Cues using Tests from Semantic Priming
Models trained to estimate word probabilities in context have become ubiquitous in natural language processing.
