38 citations · 57 across the 8 of their papers we have counts for
8 papers
LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing
Zhengxiang Wang, Veronika Makarova, Zhi Li +2
The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multipl…
Evaluating LLMs with Multiple Problems at once
Zhengxiang Wang, Jordan Kodner, Owen Rambow
This paper shows the benefits and fruitfulness of evaluating LLMs with multiple problems at once, a paradigm we call multi-problem evaluation (MPE). Unlike conventional single-prob…
Evaluating Neural Language Models as Cognitive Models of Language Acquisition
Héctor Javier Vázquez Martínez, Annika Lea Heuser, Charles Yang +1
The success of neural language models (LMs) on many technological tasks has brought about their potential relevance as scientific theories of language despite some clear difference…
Exploring Linguistic Probes for Morphological Generalization
Jordan Kodner, Salam Khalifa, Sarah Payne
Modern work on the cross-linguistic computational modeling of morphological inflection has typically employed language-independent data splitting algorithms. In this paper, we supp…
Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023)
Jordan Kodner, Sarah Payne, Jeffrey Heinz
We present a critical assessment of Piantadosi's (2023) claim that "Modern language models refute Chomsky's approach to language," focusing on four main points. First, despite the…
Morphological Inflection: A Reality Check
Jordan Kodner, Sarah Payne, Salam Khalifa +1
Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also high…
The Greedy and Recursive Search for Morphological Productivity
Caleb Belth, Sarah Payne, Deniz Beser +2
As children acquire the knowledge of their language's morphology, they invariably discover the productive processes that can generalize to new words. Morphological learning is made…
Overestimation of Syntactic Representationin Neural Language Models
Jordan Kodner, Nitish Gupta
With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so…