AIConfPaper
← Search

H. Schwartz

4 accepted papers

2025

Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm

Vasudha Varadarajan, Syeda Mahwish, Xiaoran Liu, Julia Buffolino, Christian Luhmann, Ryan L. Boyd +1
NAACL 2025short

While NLP models often seek to capture cognitive states via language, the validity of predicted states is determined by comparing them to annotations created without access the cognitive states of the authors. In behavioral sciences, cognitive states are instead measured via experiments. Here, we in…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Evaluation of LLMs-based Hidden States as Author Representations for Psychological Human-Centered NLP Tasks

Nikita Soni, Pranav Chitale, Khushboo Singh, Niranjan Balasubramanian, H. Schwartz
NAACL 2025findings

Like most of NLP, models for human-centered NLP tasks—tasks attempting to assess author-level information—predominantly use rep-resentations derived from hidden states of Transformer-based LLMs. However, what component of the LM is used for the representation varies widely. Moreover, there is a need…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies

Vishnu Raja, Adithya V Ganesan, Anand Syamkumar, Ritwik Banerjee, H. Schwartz
EMNLP 2025

State-of-the-art automatic speech recognition (ASR) models like Whisper perform poorly on atypical speech, such as that produced by individuals with dysarthria. Past works for atypical speech have mostly investigated fully personalized (or idiosyncratic) models, but modeling strategies that can both

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Residualized Similarity for Faithfully Explainable Authorship Verification

Peter Zeng, Pegah Alipoormolabashi, Jihu Mun, Gourab Dey, Nikita Soni, Niranjan Balasubramanian +2
EMNLP 2025

Responsible use of Authorship Verification (AV) systems not only requires high accuracy but also interpretable solutions. More importantly, for systems to be used to make decisions with real-world consequences requires the model’s prediction to be explainable using interpretable features that can be

Cited by 0Source ↗PDF ↗code ↗Scholar ↗