7 citations · 10 across the 7 of their papers we have counts for
8 papers
AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech
Praphul Singh, Akshat Agarwal, Ratul Ghosh +5
Clinical speech reveals identity through both spoken identifiers and voice, yet its pauses, delivery, and speaker roles can also carry useful clinical evidence. Protecting that spe…
ContractEval: Query-Conditioned Execution Matching for Procedural Instruction Conformance
Praphul Singh, Shanu Kumar, Akshat Agarwal +1
As LLM agents move from answering questions to carrying out procedures, failures can be unwarranted rather than visibly wrong: the final response looks acceptable even though the s…
Beyond Endpoint Gains: A Weight-Delta Audit of Medical Specialization
Praphul Singh, Shanu Kumar, Akshat Agarwal
Specialist language models are usually understood through endpoint gains: the generalist scores lower, the specialist scores higher, and the difference is treated as evidence of sp…
OG-Rank: Learning to Rank Fast and Slow with Uncertainty and Reward-Trend Guided Adaptive Exploration
Praphul Singh, Corey Barrett, Sumana Srivasta +3
Clinicians need ranking systems that work in real time and still justify their choices. Motivated by the need for a low-latency, decoder-based reranker, we present OG-Rank, a singl…
JEDA: Query-Free Clinical Order Search from Ambient Dialogues
Praphul Singh, Corey Barrett, Sumana Srivasta +4
Clinical conversations mix explicit directives (order a chest X-ray) with implicit reasoning (the cough worsened overnight, we should check for pneumonia). Many systems rely on LLM…
Optimizing Long-Form Clinical Text Generation with Claim-Based Rewards
Samyak Jhaveri, Praphul Singh, Jangwon Kim +2
Automating clinical documentation with large language models requires precise alignment with priorities such as completeness and factual grounding. We present an evaluation-integra…
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification
Praphul Singh, Charlotte Dzialo, Jangwon Kim +4
Ensuring clinical data privacy while preserving utility is critical for AI-driven healthcare and data analytics. Existing de-identification (De-ID) methods, including rule-based te…
Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation
Shanu Kumar, Vinod Kumar Kurmi, Praphul Singh +1
Understanding unsupervised domain adaptation has been an important task that has been well explored. However, the wide variety of methods have not analyzed the role of a classifier…