Search Results for author: Manas Gaur

Found 41 papers, 7 papers with code

A Risk-Averse Mechanism for Suicidality Assessment on Social Media

no code implementations ACL 2022 Ramit Sawhney, Atula Neerkaje, Manas Gaur

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

no code implementations NAACL (CLPsych) 2022 Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, Maria Liakata

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 .

REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs

no code implementations3 May 2024 Deepa Tilwani, Yash Saxena, Ali Mohammadi, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur

Automatic citation generation for sentences in a document or report is paramount for intelligence analysts, cybersecurity, news agencies, and education personnel.

Hallucination Retrieval +1

COBIAS: Contextual Reliability in Bias Assessment

1 code implementation22 Feb 2024 Priyanshul Govil, Vamshi Krishna Bonagiri, Manas Gaur, Ponnurangam Kumaraguru, Sanorita Dey

Our contribution is twofold: (i) we create a dataset of 2287 stereotyped statements augmented with points for adding context; (ii) we develop the Context-Oriented Bias Indicator and Assessment Score (COBIAS) to assess statements' contextual reliability in measuring bias.

SaGE: Evaluating Moral Consistency in Large Language Models

1 code implementation21 Feb 2024 Vamshi Krishna Bonagiri, Sreeram Vennam, Priyanshul Govil, Ponnurangam Kumaraguru, Manas Gaur

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.

Decision Making Moral Scenarios

Measuring Moral Inconsistencies in Large Language Models

no code implementations26 Jan 2024 Vamshi Krishna Bonagiri, Sreeram Vennam, Manas Gaur, Ponnurangam Kumaraguru

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.

Decision Making Language Modelling +2

LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge

no code implementations18 Jan 2024 Shaswata Mitra, Subash Neupane, Trisha Chakraborty, Sudip Mittal, Aritran Piplai, Manas Gaur, Shahram Rahimi

In this work, we present LOCALINTEL, a novel automated knowledge contextualization system that, upon prompting, retrieves threat reports from the global threat repositories and uses its local knowledge database to contextualize them for a specific organization.


K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries

1 code implementation29 Dec 2023 Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayanan, Manas Gaur

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.

Nutrition Response Generation +1

Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety

no code implementations5 Dec 2023 Manas Gaur, Amit Sheth

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

no code implementations23 Nov 2023 Sumit Dalal, Deepa Tilwani, Kaushik Roy, Manas Gaur, Sarika Jain, Valerie Shalin, Amit Sheth

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.

Clinical Knowledge Language Modelling

L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models

no code implementations11 Nov 2023 Aidin Shiri, Kaushik Roy, Amit Sheth, Manas Gaur

Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices.

Language Modelling STS +1

Towards Effective Paraphrasing for Information Disguise

1 code implementation8 Nov 2023 Anmol Agarwal, Shrey Gupta, Vamshi Bonagiri, Manas Gaur, Joseph Reagle, Ponnurangam Kumaraguru

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.

Ethics Sentence

Leveraging Knowledge and Reinforcement Learning for Enhanced Reliability of Language Models

no code implementations25 Aug 2023 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 training modern Language Models such as BERT.

Knowledge Graph Embeddings reinforcement-learning

Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy

no code implementations25 Jul 2023 Aritran Piplai, Anantaa Kotal, Seyedreza Mohseni, Manas Gaur, Sudip Mittal, Anupam Joshi

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.

Knowledge Graphs

IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations

no code implementations24 Jun 2023 Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth

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.

Ensemble Learning Hallucination +3

Knowledge-Infused Self Attention Transformers

no code implementations23 Jun 2023 Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth

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.

Knowledge Graphs Language Modelling

Process Knowledge-infused Learning for Clinician-friendly Explanations

no code implementations16 Jun 2023 Kaushik Roy, Yuxin Zi, Manas Gaur, Jinendra Malekar, Qi Zhang, Vignesh Narayanan, Amit Sheth

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.

Explainable Artificial Intelligence (XAI) Language Modelling

LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts

no code implementations8 Jun 2023 Muskan Garg, Manas Gaur, Raxit Goswami, Sunghwan Sohn

Low self-esteem and interpersonal needs (i. e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts.

Clinical Knowledge Data Augmentation

Neurosymbolic AI - Why, What, and How

no code implementations1 May 2023 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 knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning.

Autonomous Driving Decision Making +2

Towards Explainable and Safe Conversational Agents for Mental Health: A Survey

no code implementations25 Apr 2023 Surjodeep Sarkar, Manas Gaur, L. Chen, Muskan Garg, Biplav Srivastava, Bhaktee Dongaonkar

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

no code implementations9 Oct 2022 Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth

Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.


Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety

no code implementations9 Jun 2022 Amit Sheth, Manas Gaur, Kaushik Roy, Revathy Venkataraman, Vedant Khandelwal

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.

Food recommendation Management

Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection

no code implementations3 May 2022 Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Sakthi Balan Muthiah, Amit Sheth

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

no code implementations26 Apr 2022 Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world.

Explainable Artificial Intelligence (XAI)

ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs

1 code implementation13 Dec 2021 Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin

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.

Information Retrieval Knowledge Graphs +3

Knowledge-intensive Language Understanding for Explainable AI

no code implementations2 Aug 2021 Amit Sheth, Manas Gaur, Kaushik Roy, Keyur Faldu

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) +1

Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits

no code implementations25 Jun 2021 Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.

Descriptive Multi-Armed Bandits +2

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

no code implementations12 May 2021 Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth

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).

Natural Language Inference

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

no code implementations9 Apr 2021 Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonanthan Beich, Jyotishman Pathak, Amit Sheth

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

no code implementations11 Feb 2021 Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

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.

Decision Making

Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?

no code implementations16 Oct 2020 Manas Gaur, Keyur Faldu, Amit Sheth

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively.

Knowledge Graphs

Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak

no code implementations30 Jul 2020 Amanuel Alambo, Manas Gaur, Krishnaprasad Thirunarayan

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.

Informativeness Semantic Parsing +1

Unsupervised Detection of Sub-events in Large Scale Disasters

no code implementations13 Dec 2019 Chidubem Arachie, Manas Gaur, Sam Anzaroot, William Groves, Ke Zhang, Alejandro Jaimes

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

no code implementations1 Dec 2019 Ugur Kursuncu, Manas Gaur, Amit Sheth

Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks.

Knowledge Graphs

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

no code implementations18 Aug 2019 Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, K. Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth

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

no code implementations9 Aug 2018 Qiwei Han, Mengxin Ji, Inigo Martinez de Rituerto de Troya, Manas Gaur, Leid Zejnilovic

We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care.

Collaborative Filtering Recommendation Systems

Predictive Analysis on Twitter: Techniques and Applications

1 code implementation6 Jun 2018 Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar

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

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