46 citations · 50 across the 12 of their papers we have counts for
13 papers
FinRT: Distilling Adaptive Red-Teaming Strategies into Reusable Adversarial Generators in Consumer Finance
Rikhiya Ghosh, Himanshu Kumar, Sriram Venkatapathy +3
In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety failures and pushing responses da…
Validity-Aware Jailbreak Evaluation for Large Language Models
Qilong Wu, Sahil Wadhwa, Pranab Mohanty +2
Jailbreak robustness has become central to large language model (LLM) safety evaluation, yet prevailing methodologies rely primarily on refusal behavior, semantic resemblance, and…
Beyond the Vacuum: Combinatorial Strategy Selection for Competitor-Aware Generative Engine Optimization
Vaibhav Sourirajan, Yao Zhang, Himanshu Kumar +3
Generative Engine Optimization (GEO) has emerged as a novel paradigm for transforming content to increase visibility in Large Language Model (LLM) responses. Traditional GEO method…
Workspace Topology as an Attack Vector in Agentic Coding Assistants
Alexandre G. R. Day, Pradeep Yadlapalli, Sriram Venkatapathy +9
Agentic coding assistants are finding widespread use, not just in new code development but in quickly ingesting and leveraging third-party code. This opens up a risk of malicious c…
Adaptive Instruction Composition for Automated LLM Red-Teaming
Jesse Zymet, Andy Luo, Swapnil Shinde +2
Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with identifying effective strategies through…
STELP: Secure Transpilation and Execution of LLM-Generated Programs
Swapnil Shinde, Sahil Wadhwa, Andy Luo +2
Rapid evolution of Large Language Models (LLMs) has achieved major advances in reasoning, planning, and function-calling capabilities. Multi-agentic collaborative frameworks using…
ART: Adaptive Reasoning Trees for Explainable Claim Verification
Sahil Wadhwa, Himanshu Kumar, Guanqun Yang +4
Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. However, their adoption in h…
Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models
Alberto Purpura, Sahil Wadhwa, Jesse Zymet +5
The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems ag…
Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment
Melissa Kazemi Rad, Huy Nghiem, Andy Luo +3
Large Language Models (LLMs) have demonstrated powerful capabilities that render them valuable in different applications, including conversational AI products. It is paramount to e…
Northeastern Uni at Multilingual Counterspeech Generation: Enhancing Counter Speech Generation with LLM Alignment through Direct Preference Optimization
Sahil Wadhwa, Chengtian Xu, Haoming Chen +3
The automatic generation of counter-speech (CS) is a critical strategy for addressing hate speech by providing constructive and informed responses. However, existing methods often…
FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memory
Anwesan Pal, Sahil Wadhwa, Ayush Jaiswal +5
Multi-turn textual feedback-based fashion image retrieval focuses on a real-world setting, where users can iteratively provide information to refine retrieval results until they fi…
Contextualized End-to-End Neural Entity Linking
Haotian Chen, Andrej Zukov-Gregoric, Xi David Li +1
We propose yet another entity linking model (YELM) which links words to entities instead of spans. This overcomes any difficulties associated with the selection of good candidate m…
Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach
Hammad Haleem, Yong Wang, Abishek Puri +2
Existing graph layout algorithms are usually not able to optimize all the aesthetic properties desired in a graph layout. To evaluate how well the desired visual features are refle…