40 citations · 144 across the 27 of their papers we have counts for
33 papers
Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment
Shlok Shelat, Shrey Salvi, Souvik Roy +2
Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidence and clinically relevant risk…
SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation
Ben Lagnese, Manas Gaur
Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often underuse the relational struc…
Where do LLMs Fall Short in CBT-Guided Affective Reasoning?
Vaishnavi Sinha, Pooja Guttal, Pranay Deep Reddy Katike +5
Cognitive Behavioral Therapy (CBT) provides a structured framework for understanding a user's mental state by examining the interaction between cognitive and behavioral factors. Ho…
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
Vedant Khandelwal, Manas Gaur, Ugur Kursuncu +2
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural net…
Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
Deepa Tilwani, Yash Saxena, Seyedali Mohammadi +5
Large language models (LLMs) increasingly generate citation-backed responses, yet citation hallucination remains a major challenge for trustworthy scientific information access. We…
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…
KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts
Kaushik Roy, Yuxin Zi, Vignesh Narayanan +2
Domain-specific language understanding requires integrating multiple pieces of relevant contextual information. For example, we see both suicide and depression-related behavior (mu…
Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety
Amit Sheth, Manas Gaur, Kaushik Roy +2
AI systems have been widely adopted across various domains in the real world. However, in high-value, sensitive, or safety-critical applications such as self-management for persona…
Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts
Shrey Gupta, Anmol Agarwal, Manas Gaur +4
Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide in…
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection
Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal +2
Epidemics like Covid-19 and Ebola have impacted people's lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been si…
Process Knowledge-infused Learning for Suicidality Assessment on Social Media
Kaushik Roy, Manas Gaur, Qi Zhang +1
Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthca…