arXiv subjects

Tejomay Kishor Padole

Publications and source records attributed to Tejomay Kishor Padole.

3 recordsLinked to original sources

arXiv2026

PRiME: Pragmatic Reasoning In MEntal health

In this work, the task and domain interaction of reasoning and mental health are investigated. As large language models (LLMs) present exceptional text understanding and fluent text generation capability, we analyze whether they can reason in the high-stakes domain of mental health. We begin by introducing three datasets and three tasks leveraging the existing work in pragmatic reasoning. The analysis constitutes an evaluation of $12$ LLMs, including MentaLLaMA, which is a mental health reasoning and explanation generation model. The performance of the models remains on par with human evaluation, with the Mistral-7B model giving the best performance. We further analyze the performance of the models with error asymmetry ratio and conduct first-order attention rollout experiment with MentaLLaMA-7B. We present the results for the community reporting the architectural behaviour of the models for the input text and recommend further careful analysis for the high-stakes domain.

cs.CL↗

arXiv2026

Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts

In this work, we conduct an analysis to examine the consistency of Large Language Models (LLMs) with respect to their own generated responses in an emotionally-driven conversational context. Specifically, the text generated by LLM is framed as a query to the same model, and its responses are subsequently assessed. This is performed with three queries across two dimensions of extreme and moderate emotions. The three queries are, in particular, false claim queries that contain inherently wrong assumptions (false presuppositions) in increasing order of intensity. Two commercial models, Claude-3.5-haiku, GPT4o-mini, and a medium-sized model, Mistral-7B, are considered in the study. Our findings indicate that LLMs exhibit below-average performance and remain vulnerable to false beliefs embedded within queries. This susceptibility is especially pronounced for moderate emotional content. Furthermore, an extended attention-score-based analysis highlights a shift in models' priority from evaluative to generative. The results raise important considerations for LLMs' deployment in high-stakes, emotionally sensitive contexts.

cs.CL↗

arXiv2025

Improving Text Style Transfer using Masked Diffusion Language Models with Inference-time Scaling

Masked diffusion language models (MDMs) have recently gained traction as a viable generative framework for natural language. This can be attributed to its scalability and ease of training compared to other diffusion model paradigms for discrete data, establishing itself as the state-of-the-art non-autoregressive generator for discrete data. Diffusion models, in general, have shown excellent ability to improve the generation quality by leveraging inference-time scaling either by increasing the number of denoising steps or by using external verifiers on top of the outputs of each step to guide the generation. In this work, we propose a verifier-based inference-time scaling method that aids in finding a better candidate generation during the denoising process of the MDM. Our experiments demonstrate the application of MDMs for standard text-style transfer tasks and establish MDMs as a better alternative to autoregressive language models. Additionally, we show that a simple soft-value-based verifier setup for MDMs using off-the-shelf pre-trained embedding models leads to significant gains in generation quality even when used on top of typical classifier-free guidance setups in the existing literature.

cs.CL↗