48 citations · 59 across the 11 of their papers we have counts for
13 papers
PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust
Tanqiu Jiang, Min Bai, Sadat Shahriar +2
Persistent memory is a largely undefended surface for injection and poisoning, and the same risk limits memory sharing among agents. We present PerMIT, a memory architecture with p…
Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems
Ming Shen, Chao Shang, Sadat Shahriar +4
Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create implicit expectations of trust, s…
Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats
Sadat Shahriar, Navid Ayoobi, Arjun Mukherjee +2
The local news landscape, a vital source of reliable information for 28 million Americans, faces a growing threat from Pink Slime Journalism, a low-quality, auto-generated articles…
The Erosion of LLM Signatures: Can We Still Distinguish Human and LLM-Generated Scientific Ideas After Iterative Paraphrasing?
Sadat Shahriar, Navid Ayoobi, Arjun Mukherjee
With the increasing reliance on LLMs as research agents, distinguishing between LLM and human-generated ideas has become crucial for understanding the cognitive nuances of LLMs' re…
Beyond Easy Wins: A Text Hardness-Aware Benchmark for LLM-generated Text Detection
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
We present a novel evaluation paradigm for AI text detectors that prioritizes real-world and equitable assessment. Current approaches predominantly report conventional metrics like…
Inference time LLM alignment in single and multidomain preference spectrum
Sadat Shahriar, Zheng Qi, Nikolaos Pappas +5
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-con…
Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
LLMs offer valuable capabilities, yet they can be utilized by malicious users to disseminate deceptive information and generate fake news. The growing prevalence of LLMs poses diff…
HU at SemEval-2024 Task 8A: Can Contrastive Learning Learn Embeddings to Detect Machine-Generated Text?
Shubhashis Roy Dipta, Sadat Shahriar
This paper describes our system developed for SemEval-2024 Task 8, ``Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection'' Machine-generated te…
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration an…
SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation
Sadat Shahriar, Thamar Solorio
Subjectivity and difference of opinion are key social phenomena, and it is crucial to take these into account in the annotation and detection process of derogatory textual content.…
Deception Detection with Feature-Augmentation by soft Domain Transfer
Sadat Shahriar, Arjun Mukherjee, Omprakash Gnawali
In this era of information explosion, deceivers use different domains or mediums of information to exploit the users, such as News, Emails, and Tweets. Although numerous research h…
Improving Phishing Detection Via Psychological Trait Scoring
Sadat Shahriar, Arjun Mukherjee, Omprakash Gnawali
Phishing emails exhibit some unique psychological traits which are not present in legitimate emails. From empirical analysis and previous research, we find three psychological trai…
A Domain-Independent Holistic Approach to Deception Detection
Sadat Shahriar, Arjun Mukherjee, Omprakash Gnawali
The deception in the text can be of different forms in different domains, including fake news, rumor tweets, and spam emails. Irrespective of the domain, the main intent of the dec…