Author name cluster

Manas Gaur

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

TIST Journal 2026 Journal Article

Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context

Nilanjana Das
Edward Raff
Aman Chadha
Manas Gaur

Large Language Models (LLMs) remain vulnerable to adversarial prompts that elicit harmful responses. While many existing safety systems are better able to detect overtly nonsensical attack strings, human-readable prompts embedded in plausible situational contexts remain harder to identify and evaluate. This paper presents an empirical investigation of human-readable, situation-driven adversarial prompts for assessing LLM robustness. First, we use movie scripts as situational contexts (e.g., crime narratives) to construct natural-looking prompts that bypass safety mechanisms. Second, we transform adversarial gibberish into coherent, innocuous-appearing text that retains exploitation capability within these contextual frameworks. Third, we enhance the AdvPrompter framework with p-nucleus sampling to generate diverse human-readable attacks, substantially improving success rates against models including GPT-3.5 and Gemma-7b. We validate our approach through multi-method evaluation: automated harmfulness scoring (GPT-4o-mini), independent human assessment by 10 raters across 80 samples, and Elo rating meta-analysis of judge reliability. These findings highlight the need for safety mechanisms that can detect not only nonsensical jailbreak strings, but also coherent adversarial content embedded in realistic narrative contexts. The code for this study is publicly available. 1

Details DOI

IS Journal 2026 Journal Article

Neurosymbolic Retrievers for Retrieval-Augmented Generation

Yash Saxena
Manas Gaur

Retrieval-augmented generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and issues with transparency. This new framework aims to answer two primary questions: 1) Can retrievers provide a clear and interpretable basis for document selection? 2) Can symbolic knowledge enhance the clarity of the retrieval process? We propose three methods to improve this integration. The first is modulation augmented retrieval which employs modulation networks to refine query embeddings using interpretable symbolic features, thereby making document matching more explicit. The second is KG-Path RAG, which enhances queries by traversing knowledge graphs to improve overall retrieval quality and interpretability. Finally, process knowledge-infused RAG utilizes domain-specific tools to reorder retrieved content based on validated workflows. Preliminary results from mental health risk assessment tasks indicate that this neurosymbolic approach enhances both transparency and overall performance.

Details DOI

JBHI Journal 2025 Journal Article

A Cross Attention Approach to Diagnostic Explainability Using Clinical Practice Guidelines for Depression

Sumit Dalal
Deepa Tilwani
Manas Gaur
Sarika Jain
Valerie L. Shalin
Amit P. Sheth

The lack of explainability in using relevant clinical knowledge hinders the adoption of artificial intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevant, untapped Mental Health (MH) data is available in online communities, providing the opportunity to address the explainability problem with substantial potential impact as a screening tool for both online and offline applications. Inspired by how clinicians rely on their expertise when interacting with patients, we leverage relevant clinical knowledge to classify and explain depression-related data, reducing manual review time and engendering trust. We developed a method to enhance attention in contemporary transformer models and generate explanations for classifications that are understandable by mental health practitioners (MHPs) by incorporating external clinical knowledge. We propose a domain-general architecture called P roces S knowledge-infused cross AT tention ( PSAT ) that incorporates clinical practice guidelines (CPG) when computing attention. We transform a CPG resource focused on depression, such as the Patient Health Questionnaire (e. g. PHQ-9) and related questions, into a machine-readable ontology using SNOMED-CT. With this resource, PSAT enhances the ability of models like GPT-3. 5 to generate application-relevant explanations. Evaluation of four expert-curated datasets related to depression demonstrates PSAT ’s application-relevant explanations. PSAT surpasses the performance of twelve baseline models and can provide explanations where other baselines fall short.

Details DOI

AAAI Conference 2025 Conference Paper

Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation

Seyedreza Mohseni
Seyedali Mohammadi
Deepa Tilwani
Yash Saxena
Gerald Ketu Ndawula
Sriram Vema
Edward Raff
Manas Gaur

Malware authors often employ code obfuscations to make their malware harder to detect. Existing tools for generating obfuscated code often require access to the original source code (e.g., C++ or Java), and adding new obfuscations is a non-trivial, labor-intensive process. In this study, we ask the following question: Can Large Language Models (LLMs) potentially generate a new obfuscated assembly code? If so, this poses a risk to anti-virus engines and potentially increases the flexibility of attackers to create new obfuscation patterns. We answer this in the affirmative by developing the MetamorphASM benchmark comprising MetamorphASM Dataset (MAD) along with three code obfuscation techniques: dead code, register substitution, and control flow change. The MetamorphASM systematically evaluates the ability of LLMs to generate and analyze obfuscated code using MAD, which contains 328,200 obfuscated assembly code samples. We release this dataset and analyze the success rate of various LLMs (e.g., GPT-3.5/4, GPT-4o-mini, Starcoder, CodeGemma, CodeLlama, CodeT5, and LLaMA 3.1) in generating obfuscated assembly code. The evaluation was performed using established information-theoretic metrics and manual human review to ensure correctness and provide the foundation for researchers to study and develop remediations to this risk.

PDF Details DOI

IS Journal 2023 Journal Article

Neurosymbolic Artificial Intelligence (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 knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of artificial intelligence (AI), refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision making in safety-critical applications such as health care, criminal justice, and autonomous driving.

Details DOI

AAAI Conference 2023 Conference Paper

Targeted Knowledge Infusion To Make Conversational AI Explainable and Safe

Manas Gaur

Conversational Systems (CSys) represent practical and tangible outcomes of advances in NLP and AI. CSys see continuous improvements through unsupervised training of large language models (LLMs) on a humongous amount of generic training data. However, when these CSys are suggested for use in domains like Mental Health, they fail to match the acceptable standards of clinical care, such as the clinical process in Patient Health Questionnaire (PHQ-9). The talk will present, Knowledge-infused Learning (KiL), a paradigm within NeuroSymbolic AI that focuses on making machine/deep learning models (i) learn over knowledge-enriched data, (ii) learn to follow guidelines in process-oriented tasks for safe and reasonable generation, and (iii) learn to leverage multiple contexts and stratified knowledge to yield user-level explanations. KiL established Knowledge-Intensive Language Understanding, a set of tasks for assessing safety, explainability, and conceptual flow in CSys.

PDF Details DOI

AAAI Conference 2022 Conference Paper

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

Manas Gaur
Kalpa Gunaratna
Vijay Srinivasan
Hongxia Jin

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the enduser. 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. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generativeadversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. The qualitative human evaluation confirms ISEEQ-generated ISQs are comparable in quality to human-generated questions and outperform the best comparable baseline.

PDF Details

AAAI Conference 2020 Conference Paper

Unsupervised Detection of Sub-Events in Large Scale Disasters

Chidubem Arachie
Manas Gaur
Sam Anzaroot
William Groves
Ke Zhang
Alejandro Jaimes

Social media plays a major role during and after major natural disasters (e. g. , hurricanes, large-scale ﬁres, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in ﬁnding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We ﬁrst extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-speciﬁc ontology. We ﬁlter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

PDF Details