6 citations · 12 across the 16 of their papers we have counts for
18 papers
Why Is SHAP Not a Reliable Standalone Explanation Framework for Malware Detection?
Seyedreza Mohseni, Edward Raff, Manas Gaur
Machine learning is widely used for malware detection, but its decisions must be explained. An analyst needs to know whether a model has learned genuine malicious behavior or only…
To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion
Shaswati Saha, Rajasekhar Anguluri, Manas Gaur
Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while preserving model utility on benig…
Structure-Aware Chunking for Tabular Data in Retrieval-Augmented Generation
Pooja Guttal, Varun Magotra, Vasudeva Mahavishnu +3
Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented generation (RAG) are primarily…
Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks
Seyedreza Mohseni, Sarvesh Baskar, Edward Raff +1
Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires days or even months of manual…
From Concept-Aligned Tokens to Vulnerable Features: Mechanistic Localization of Jailbreaks
Nilanjana Das, Mathew Dawit, Aman Chadha +1
Jailbreak attacks expose a persistent failure mode in safety-aligned LLMs: models can be pushed into harmful behavior, but the internal representations enabling this shift remain p…
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 i…
IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation
Yash Saxena, Ankur Padia, Kalpa Gunaratna +1
Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is…
Side Effects of Erasing Concepts from Diffusion Models
Shaswati Saha, Sourajit Saha, Manas Gaur +1
Concerns about text-to-image (T2I) generative models infringing on privacy, copyright, and safety have led to the development of concept erasure techniques (CETs). The goal of an e…
Generation-Time vs. Post-hoc Citation: A Holistic Evaluation of LLM Attribution
Yash Saxena, Raviteja Bommireddy, Ankur Padia +1
Trustworthy Large Language Models (LLMs) must cite human-verifiable sources in high-stakes domains such as healthcare, law, academia, and finance, where even small errors can have…
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba +4
Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across mu…
Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
Yash Saxena, Ankur Padia, Mandar S Chaudhary +3
Retrieval-Augmented Generation (RAG) systems deployed in sensitive domains must provide interpretable evidence selection and robust safeguards against data poisoning, yet current a…
From Guessing to Asking: An Approach to Resolving the Persona Knowledge Gap in LLMs during Multi-Turn Conversations
Sarvesh Baskar, Tanmay Tulsidas Verelakar, Srinivasan Parthasarathy +1
In multi-turn dialogues, large language models (LLM) face a critical challenge of ensuring coherence while adapting to user-specific information. This study introduces the persona…
Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation
Seyedreza Mohseni, Seyedali Mohammadi, Deepa Tilwani +5
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 cod…
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
Nilanjana Das, Edward Raff, Aman Chadha +1
As the AI systems become deeply embedded in social media platforms, we've uncovered a concerning security vulnerability that goes beyond traditional adversarial attacks. It becomes…
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
Abhinav Joshi, Shaswati Saha, Divyaksh Shukla +4
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. LLMs have been trained on a vas…
Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context
Nilanjana Das, Edward Raff, Manas Gaur
Previous research on testing the vulnerabilities in Large Language Models (LLMs) using adversarial attacks has primarily focused on nonsensical prompt injections, which are easily…
IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being
Amelie Gyrard, Seyedali Mohammadi, Manas Gaur +1
Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-…
WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Seyedali Mohammadi, Edward Raff, Jinendra Malekar +3
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 te…