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Found 49 papers, 10 papers with code
A Risk-Averse Mechanism for Suicidality Assessment on Social Media
Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings.
Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts
We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health .
Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation
The MetamorphASM systematically evaluates the ability of LLMs to generate and analyze obfuscated code using MAD, which contains 328, 200 obfuscated assembly code samples.
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
Previous research on LLM vulnerabilities often relied on nonsensical adversarial prompts, which were easily detectable by automated methods.
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents.
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment.
Unboxing Occupational Bias: Grounded Debiasing of LLMs with U.S. Labor Data
Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gender, occupation, and other sensitive categories.
Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context
Previous research on testing the vulnerabilities in Large Language Models (LLMs) using adversarial attacks has primarily focused on nonsensical prompt injections, which are easily detected upon manual or automated review (e. g., via byte entropy).
IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being
Standards from ETSI SmartM2M can be used such as SAREF4EHAW to represent medical devices and sensors, but also ITU/WHO, ISO, W3C, NIST, and IEEE standards relevant to mental health can be considered.
WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus test of a model's utility in clinical practice.
Attribution in Scientific Literature: New Benchmark and Methods
Retrieval-augmented generation (RAG) with Mistral improves performance in indirect queries, reducing hallucination rates by 42% and maintaining competitive precision with larger models.
COBIAS: Contextual Reliability in Bias Assessment
We develop the Context-Oriented Bias Indicator and Assessment Score (COBIAS) to measure a biased statement's reliability in detecting bias based on the variance in model behavior across different contexts.
SaGE: Evaluating Moral Consistency in Large Language Models
To this extent, we construct the Moral Consistency Corpus (MCC), containing 50K moral questions, responses to them by LLMs, and the RoTs that these models followed.
Measuring Moral Inconsistencies in Large Language Models
To address this issue, we propose a novel information-theoretic measure called Semantic Graph Entropy (SGE) to measure the consistency of an LLM in moral scenarios.
LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge
SoCs undertake a manual labor-intensive task of utilizing these global threat repositories and local knowledge databases to create both organization-specific threat intelligence and mitigation policies.
K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries
To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source.
Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety
We present the CREST framework that shows how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well-being.
A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression
We develop such a system in the context of MH using clinical practice guidelines (CPG) for diagnosing depression, a mental health disorder of global concern.
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models
Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices.
Towards Effective Paraphrasing for Information Disguise
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-consensual use of authors' posts on the Internet.
Leveraging Knowledge and Reinforcement Learning for Enhanced Reliability of Language Models
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 training modern Language Models such as BERT.
Simple is Better and Large is Not Enough: Towards Ensembling of Foundational Language Models
Foundational Language Models (FLMs) have advanced natural language processing (NLP) research.
Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy
Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to enhance explainability and safety in AI systems.
IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations
Crowdsourced and expert-curated knowledge graphs such as ConceptNet are designed to capture the meaning of words from a compact set of well-defined contexts.
Knowledge-Infused Self Attention Transformers
However, the ad-hoc nature of existing methods makes it difficult to properly analyze the effects of knowledge infusion on the many moving parts or components of a transformer.
Process Knowledge-infused Learning for Clinician-friendly Explanations
In this study, we introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm that layers clinical process knowledge structures on language model outputs, enabling clinician-friendly explanations of the underlying language model predictions.
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts
Low self-esteem and interpersonal needs (i. e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts.
ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain.
Neurosymbolic AI - Why, What, and How
Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning.
Towards Explainable and Safe Conversational Agents for Mental Health: A Survey
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 million Emergency Room (ER) visits annually.
KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts
Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.
Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety
For such applications, in addition to data and domain knowledge, the AI systems need to have access to and use the Process Knowledge, an ordered set of steps that the AI system needs to use or adhere to.
Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection
In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model.
Process Knowledge-infused Learning for Suicidality Assessment on Social Media
Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world.
ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs
To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query.
Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems
Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress.
Knowledge-intensive Language Understanding for Explainable AI
To understand and validate an AI system's outcomes (such as classification, recommendations, predictions), that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use.
Decision Making
Explainable Artificial Intelligence (XAI)
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Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits
Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.
"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit
During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs).
Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS).
Knowledge Infused Policy Gradients for Adaptive Pandemic Control
To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner.
Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively.
Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak
Further, apart from providing informative content to the public, the incessant media coverage of COVID-19 crisis in terms of news broadcasts, published articles and sharing of information on social media have had the undesired snowballing effect on stress levels (further elevating depression and drug use) due to uncertain future.
Unsupervised Detection of Sub-events in Large Scale Disasters
Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable.
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks.
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming.
A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care
We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care.
Predictive Analysis on Twitter: Techniques and Applications
Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide.
Social and Information Networks
