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.
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 .
no code implementations • 3 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.
1 code implementation • 22 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.
1 code implementation • 21 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.
no code implementations • 26 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.
no code implementations • 18 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.
1 code implementation • 29 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.
no code implementations • 5 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.
no code implementations • 23 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.
no code implementations • 11 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.
1 code implementation • 8 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.
no code implementations • 25 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.
no code implementations • 23 Aug 2023 • Nancy Tyagi, Aidin Shiri, Surjodeep Sarkar, Abhishek Kumar Umrawal, Manas Gaur
Foundational Language Models (FLMs) have advanced natural language processing (NLP) research.
no code implementations • 25 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.
no code implementations • 24 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.
no code implementations • 23 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.
no code implementations • 16 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
no code implementations • 8 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.
no code implementations • 13 May 2023 • Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Kalyan, Amit Sheth
LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain.
no code implementations • 1 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.
no code implementations • 25 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.
no code implementations • 9 Oct 2022 • Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth
Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.
no code implementations • 9 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.
1 code implementation • NAACL (CLPsych) 2022 • Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
no code implementations • 3 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.
no code implementations • 26 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.
1 code implementation • 13 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.
no code implementations • 29 Oct 2021 • Keyur Faldu, Amit Sheth, Prashant Kikani, Manas Gaur, Aditi Avasthi
Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress.
no code implementations • 2 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
no code implementations • 25 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.
no code implementations • 12 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).
no code implementations • 9 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).
no code implementations • 11 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.
no code implementations • 16 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.
no code implementations • 30 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.
no code implementations • 13 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.
no code implementations • 1 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.
no code implementations • 18 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.
no code implementations • 9 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.
1 code implementation • 6 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