19 citations · 36 across the 11 of their papers we have counts for
6 papers · 1 filter
SaGE: Evaluating Moral Consistency in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Priyanshul Govil +2
Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally in…
Measuring Moral Inconsistencies in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Manas Gaur +1
A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impre…
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models
Aidin Shiri, Kaushik Roy, Amit Sheth +1
Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices. This necessitates the development o…
Leveraging Knowledge and Reinforcement Learning for Enhanced Reliability of Language Models
Nancy Tyagi, Surjodeep Sarkar, Manas Gaur
The Natural Language Processing(NLP) community has been using crowd sourcing techniques to create benchmark datasets such as General Language Understanding and Evaluation(GLUE) for…
Simple is Better and Large is Not Enough: Towards Ensembling of Foundational Language Models
Nancy Tyagi, Aidin Shiri, Surjodeep Sarkar +2
Foundational Language Models (FLMs) have advanced natural language processing (NLP) research. Current researchers are developing larger FLMs (e.g., XLNet, T5) to enable contextuali…
ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
Kaushik Roy, Manas Gaur, Misagh Soltani +3
Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constr…