222 citations · 238 across the 17 of their papers we have counts for
19 papers
MEA: A Reward-Driven Multi-Agent System for Faithful Model Explanations
Yuyang Cheng, Raghav Kaushik Ravi, Srivarshinee Sridhar +3
Recent years have seen the employment of a plethora of machine learning (ML) models in high-stakes domains, but they remain largely opaque to the practitioners who act on their pre…
GenoMorph: Pathway-Grounded Genomic Disease Reasoning via Adaptive Latent Computation
Tanmoy Kanti Halder, Akash Ghosh, Arijit Roy +1
Large language models (LLMs) have demonstrated strong capabilities in biological reasoning; however, genomic disease inference remains largely dependent on memorized gene-disease a…
MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domain
Sourav Malakar, Harshit Nigam, Akash Ghosh +4
Time-series data in clinical settings is crucial for capturing dynamic changes in a patient's health over time, enabling timely diagnosis, personalized treatment, and early detecti…
ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages
Tanmoy Kanti Halder, Akash Ghosh, Subhadip Baidya +2
Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healt…
LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
Ashutosh Tripathi, Surya Deep Singh, Pranab Sahoo +1
Low-Rank Adaptation is widely used for parameter-efficient fine-tuning, yet existing methods typically assign the same adapter rank to every transformer layer despite their heterog…
CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare
Akash Ghosh, Tajamul Ashraf, Rishu Kumar Singh +4
Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks. However, recent efforts have…
CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning
Eric Onyame, Akash Ghosh, Subhadip Baidya +3
While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning appli…
CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare
Akash Ghosh, Srivarshinee Sridhar, Raghav Kaushik Ravi +3
Integrating language models (LMs) in healthcare systems holds great promise for improving medical workflows and decision-making. However, a critical barrier to their real-world ado…
Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone
Shaivi Malik, Hasnat Md Abdullah, Sriparna Saha +1
As Vision Language Models (VLMs) become integral to real-world applications, understanding their demographic biases is critical. We introduce GRAS, a benchmark for uncovering demog…
Infogen: Generating Complex Statistical Infographics from Documents
Akash Ghosh, Aparna Garimella, Pritika Ramu +2
Statistical infographics are powerful tools that simplify complex data into visually engaging and easy-to-understand formats. Despite advancements in AI, particularly with LLMs, ex…
Relic: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples
Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh +4
Reward models are essential for aligning large language models (LLMs) with human preferences. However, most open-source multilingual reward models are primarily trained on preferen…
A Survey of Multilingual Reasoning in Language Models
Akash Ghosh, Debayan Datta, Sriparna Saha +1
While reasoning and multilingual capabilities in language models (LMs) have achieved remarkable progress in recent years, their integration into a unified paradigm - multilingual r…
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning
Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha +1
Federated Learning (FL) marks a transformative approach to distributed model training by combining locally optimized models from various clients into a unified global model. While…
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging
Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha +1
Despite recent advancements in federated learning (FL) for medical image diagnosis, addressing data heterogeneity among clients remains a significant challenge for practical implem…
Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha +2
The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance, enhancing patient safety by identifying potential risks associated with medications, facilitating early de…
ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos
Krishanu Maity, A. S. Poornash, Sriparna Saha +1
In an era of rapidly evolving internet technology, the surge in multimodal content, including videos, has expanded the horizons of online communication. However, the detection of t…
A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models
Pranab Sahoo, Prabhash Meharia, Akash Ghosh +3
The rapid advancement of foundation models (FMs) across language, image, audio, and video domains has shown remarkable capabilities in diverse tasks. However, the proliferation of…
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions
Akash Ghosh, Arkadeep Acharya, Sriparna Saha +2
The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primar…
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha +3
Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverag…