40 citations · 144 across the 27 of their papers we have counts for
14 papers · 1 filter
Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety
Manas Gaur, Amit Sheth
Explainability and Safety engender Trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyze data and knowledge with…
K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries
Kanak Raj, Kaushik Roy, Vamshi Bonagiri +3
Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to appropriately tend to a us…
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…
Towards Effective Paraphrasing for Information Disguise
Anmol Agarwal, Shrey Gupta, Vamshi Bonagiri +3
Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensu…
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…
Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy
Aritran Piplai, Anantaa Kotal, Seyedreza Mohseni +3
Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic know…
IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations
Yuxin Zi, Kaushik Roy, Vignesh Narayanan +2
Large Language Models (LLMs) encode meanings of words in the form of distributed semantics. Distributed semantics capture common statistical patterns among language tokens (words,…
Knowledge-Infused Self Attention Transformers
Kaushik Roy, Yuxin Zi, Vignesh Narayanan +2
Transformer-based language models have achieved impressive success in various natural language processing tasks due to their ability to capture complex dependencies and contextual…
Process Knowledge-infused Learning for Clinician-friendly Explanations
Kaushik Roy, Yuxin Zi, Manas Gaur +4
Language models have the potential to assess mental health using social media data. By analyzing online posts and conversations, these models can detect patterns indicating mental…
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts
Muskan Garg, Manas Gaur, Raxit Goswami +1
Low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts. Individuals se…
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…
Neurosymbolic AI -- Why, What, and How
Amit Sheth, Kaushik Roy, Manas Gaur
Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowled…
Towards Explainable and Safe Conversational Agents for Mental Health: A Survey
Surjodeep Sarkar, Manas Gaur, L. Chen +3
Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 mill…