Yan Cong
· Assistant ProfessorPurdue University · SLC
Active 1988–2025
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About
Yan Cong is an Assistant Professor at the College of Liberal Arts at Purdue University, working within the SLC department. Her research focuses on language in humans and machines, specifically in the areas of natural language processing (NLP), semantics and pragmatics, and speech and language technology. She is developing text analysis models aimed at quantifying, understanding, and improving language learning. Yan Cong's background is rooted in linguistics, with a PhD from Michigan State University, and she has previously worked as an NLP researcher at the Feinstein Institutes. Her work involves assessing semantic and pragmatic competence in large language models, exploring Chinese linguistics, and applying computational approaches to speech and language fluency. She uses formal derivations to model meaning, its interaction with structure, and how speakers and listeners reason about meaning. Yan Cong is interested in using computational methods to explore linguistic questions and to inform the design of AI systems. Her core interests include the science of language and its potential applications, such as language education and healthcare. She aims to translate NLP and AI methods to help learners, teachers, and practitioners, and to improve AI systems' flexibility and transparency by assessing linguistic capacities and teasing apart true understanding from heuristic strategies. Her goal is to develop robust AI systems that serve as reliable tools for language research and…
Research topics
- Computer Science
- Internal medicine
- Natural Language Processing
- Linguistics
- Cancer research
- Medicine
- Artificial Intelligence
- Sociology
- Surgery
- Philosophy
Selected publications
Clinical efficacy of pre-trained large language models through the lens of aphasia
Scientific Reports · 2024-07-06 · 21 citations
articleOpen access1st authorCorrespondingThe rapid development of large language models (LLMs) motivates us to explore how such state-of-the-art natural language processing systems can inform aphasia research. What kind of language indices can we derive from a pre-trained LLM? How do they differ from or relate to the existing language features in aphasia? To what extent can LLMs serve as an interpretable and effective diagnostic and measurement tool in a clinical context? To investigate these questions, we constructed predictive and co…
Demystifying large language models in second language development research
Computer Speech & Language · 2024-07-26 · 13 citations
articleOpen access1st authorCorrespondingEvaluating students' textual response is a common and critical task in language research and education practice. However, manual assessment can be tedious and may lack consistency, posing challenges for both scientific discovery and frontline teaching. Leveraging state-of-the-art large language models (LLMs), we aim to define and operationalize LLM-Surprisal, a numeric representation of the interplay between lexical diversity and syntactic complexity, and to empirically and theoretically demonst…
Manner implicatures in large language models
Scientific Reports · 2024-11-24 · 10 citations
articleOpen access1st authorCorrespondingAbstract In human speakers’ daily conversations, what we do not say matters. We not only compute the literal semantics but also go beyond and draw inferences from what we could have said but chose not to. How well is this pragmatic reasoning process represented in pre-trained large language models (LLM)? In this study, we attempt to address this question through the lens of manner implicature, a pragmatic inference triggered by a violation of the Grice manner maxim. Manner implicature is a centr…
AI Language Models: An Opportunity to Enhance Language Learning
Informatics · 2024-07-19 · 7 citations
articleOpen access1st authorCorrespondingAI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance second language learning and teaching? In order to quantitatively characterize and index second language writing, the current work proposes the use of similarities derived from contextualized meaning representations in AI language models. The computat…
On the influence of discourse connectives on the predictions of humans and language models
Frontiers in Human Neuroscience · 2024-09-30 · 5 citations
articleOpen accessPsycholinguistic literature has consistently shown that humans rely on a rich and organized understanding of event knowledge to predict the forthcoming linguistic input during online sentence comprehension. We, the authors, expect sentences to maintain coherence with the preceding context, making congruent sentence sequences easier to process than incongruent ones. It is widely known that discourse relations between sentences (e.g., temporal, contingency, comparison) are generally made explicit…
Frequent coauthors
- 18 shared
Sarah Berretta
The University of Texas at Dallas
- 18 shared
Sunny X. Tang
Northwell Health
- 16 shared
Mark Liberman
- 13 shared
Sunghye Cho
Pennsylvania Academic Library Consortium
- 11 shared
Majnu John
- 9 shared
Katrin Hänsel
Northwell Health
- 9 shared
Gwenyth Mercep
Feinstein Institute for Medical Research
- 9 shared
Amir H. Nikzad
Feinstein Institute for Medical Research
Education
PhD, Linguistics
Michigan State University
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