8 papers
OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs
Suraj Racha, Prashant Harish Joshi, Utkarsh Maurya +6
Large Language Models (LLMs) have shown remarkable capabilities for complex tasks, yet adaptation in medical domain, specifically mental health, poses specific challenges. Mental h…
BhashaBench V1: A Comprehensive Benchmark for the Quadrant of Indic Domains
Vijay Devane, Mohd Nauman, Bhargav Patel +14
The rapid advancement of large language models(LLMs) has intensified the need for domain and culture specific evaluation. Existing benchmarks are largely Anglocentric and domain-ag…
PARAM-1 BharatGen 2.9B Model
Kundeshwar Pundalik, Piyush Sawarkar, Nihar Sahoo +19
Large Language Models (LLMs) have emerged as powerful general-purpose reasoning systems, yet their development remains dominated by English-centric data, architectures, and optimiz…
Inducing Robustness in a 2 Dimensional Direct Preference Optimization Paradigm
Sarvesh Shashidhar, Ritik, Nachiketa Patil +2
Direct Preference Optimisation (DPO) has emerged as a powerful method for aligning Large Language Models (LLMs) with human preferences, offering a stable and efficient alternative…
Subset Selection for Fine-Tuning: A Utility-Diversity Balanced Approach for Mathematical Domain Adaptation
Madhav Kotecha, Vijendra Kumar Vaishya, Smita Gautam +1
We propose a refined approach to efficiently fine-tune large language models (LLMs) on specific domains like the mathematical domain by employing a budgeted subset selection method…
MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models
Suraj Racha, Prashant Joshi, Anshika Raman +4
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 app…