arXiv subjects

Prashant Joshi

Publications and source records attributed to Prashant Joshi.

2 recordsLinked to original sources

arXiv2025

Estimating the Diffuseness for the Non-Relaxor Type Ferroelectric to Paraelectric Phase Transition in BaTiO3

The normal ferroelectric to paraelectric phase transition in model ferroelectric materials such as BaTiO3 is typically characterized by a sharp, well-defined dielectric constant peak at a specific transition temperature. However, under certain modifications of the parent material, this transition can become diffuse over a broad range of temperatures. This has garnered significant research attention over the past few decades, primarily because of its intriguing and not yet fully understood physical properties. The parameters developed to measure the diffuseness are also ambiguous. In this work, an investigation has been conducted to understand the transition dynamics of the non-relaxor ferroelectric systems in the temperature interval over which the diffuse phase transition occurs. This is achieved by modelling the dielectric response phenomenologically, using a distribution of local transition temperatures. Moreover, a temperature dependent differential analysis is introduced, enabling the clear demarcation of distinct dielectric regimes and providing enhanced insight into the evolution of the phase transition. It is strongly recommended that the degree of diffuseness is skeptical in many senses. Hence, following the establishment, a simple yet effective new measure of diffuseness is being proposed, offering a more accurate and physically meaningful estimation of the diffuse phase transition.

cond-mat.mtrl-sci↗

arXiv2025

MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models

Mental health remains a challenging problem all over the world, with issues like depression, anxiety becoming increasingly common. Large Language Models (LLMs) have seen a vast application in healthcare, specifically in answering medical questions. However, there is a lack of standard benchmarking datasets for question answering (QA) in mental health. Our work presents a novel multiple choice dataset, MHQA (Mental Health Question Answering), for benchmarking Language models (LMs). Previous mental health datasets have focused primarily on text classification into specific labels or disorders. MHQA, on the other hand, presents question-answering for mental health focused on four key domains: anxiety, depression, trauma, and obsessive/compulsive issues, with diverse question types, namely, factoid, diagnostic, prognostic, and preventive. We use PubMed abstracts as the primary source for QA. We develop a rigorous pipeline for LLM-based identification of information from abstracts based on various selection criteria and converting it into QA pairs. Further, valid QA pairs are extracted based on post-hoc validation criteria. Overall, our MHQA dataset consists of 2,475 expert-verified gold standard instances called MHQA-gold and ~56.1k pairs pseudo labeled using external medical references. We report F1 scores on different LLMs along with few-shot and supervised fine-tuning experiments, further discussing the insights for the scores.

cs.CL↗