46 citations · 86 across the 27 of their papers we have counts for
16 papers · 1 filter
Closing Ambient Clinical Documentation Gaps with Automated Provider Queries
Joseph Paul Cohen, Raj Shah, Han-Chin Shing +4
Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate billing. Prior work automat…
On the use of foundation models in cognitive science
Raj Sanjay Shah, Alex Warstadt, Michael Frank +1
A host of recent studies have evaluated the cognitive and developmental alignment of Foundation Models (FMs). These investigations include evaluations of their correspondence to ad…
Human-Like Anaphor Resolution in Large Language Models
Keane Zhang, Varshini Chinta, Raj Sanjay Shah +1
Anaphors are expressions that refer to other expressions, called antecedents. The process of connecting the two is called resolution. Cognitive science has identified multiple fact…
BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop
Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul +7
The goal of the BabyLM is to stimulate new research connections between cognitive modeling and language model pretraining. We invite contributions in this vein to the BabyLM Worksh…
The World According to LLMs: How Geographic Origin Influences LLMs' Entity Deduction Capabilities
Harsh Nishant Lalai, Raj Sanjay Shah, Jiaxin Pei +3
Large Language Models (LLMs) have been extensively tuned to mitigate explicit biases, yet they often exhibit subtle implicit biases rooted in their pre-training data. Rather than d…
TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of Behavioral Therapy Notes
Raj Sanjay Shah, Lei Xu, Qianchu Liu +3
Behavioral therapy notes are important for both legal compliance and patient care. Unlike progress notes in physical health, quality standards for behavioral therapy notes remain u…
BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop
Lucas Charpentier, Leshem Choshen, Ryan Cotterell +11
BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 3rd BabyLM competition. As…
The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories
Raj Sanjay Shah, Sashank Varma
Many studies have evaluated the cognitive alignment of Pre-trained Language Models (PLMs), i.e., their correspondence to adult performance across a range of cognitive domains. Rece…
Development of Cognitive Intelligence in Pre-trained Language Models
Raj Sanjay Shah, Khushi Bhardwaj, Sashank Varma
Recent studies show evidence for emergent cognitive abilities in Large Pre-trained Language Models (PLMs). The increasing cognitive alignment of these models has made them candidat…
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Jiangshu Du, Yibo Wang, Wenting Zhao +37
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…
From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models
Harsh Nishant Lalai, Aashish Anantha Ramakrishnan, Raj Sanjay Shah +1
With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. Watermarking offers a vital solution, protecting both - LLM…
Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention
Andrew Li, Xianle Feng, Siddhant Narang +4
When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in ps…
Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors
Alicja Chaszczewicz, Raj Sanjay Shah, Ryan Louie +3
Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on hum…
Pre-training LLMs using human-like development data corpus
Khushi Bhardwaj, Raj Sanjay Shah, Sashank Varma
Pre-trained Large Language Models (LLMs) have shown success in a diverse set of language inference and understanding tasks. The pre-training stage of LLMs looks at a large corpus o…
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
Raj Sanjay Shah, Kunal Chawla, Dheeraj Eidnani +7
Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training s…
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning
Yash Jakhotiya, Vaibhav Kumar, Ashwin Pathak +1
Large Language Models have been successful in a wide variety of Natural Language Processing tasks by capturing the compositionality of the text representations. In spite of their g…