activity
20242026
most citedValidate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification

2 citations · 2 across the 1 of their papers we have counts for

collaborators

7 papers

cs.CL20262 cited

Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification

M. Mikail Demir, M. Abdullah Canbaz

Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant risk. To address the shortcomi…

cs.CR2025

Who Coordinates U.S. Cyber Defense? A Co-Authorship Network Analysis of Joint Cybersecurity Advisories (2024--2025)

M. Abdullah Canbaz, Hakan Otal, Tugce Unlu +2

Cyber threats increasingly demand joint responses, yet the organizational dynamics behind multi-agency cybersecurity collaboration remain poorly understood. Understanding who leads…

cs.IR2025

Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach

Mahsa Goodarzi, M. Abdullah Canbaz

Bias in recommender systems not only distorts user experience but also perpetuates and amplifies existing societal stereotypes, particularly in sectors like fashion e-commerce. Thi…

cs.AI2025

Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI

Payam Saeedi, Mahsa Goodarzi, M Abdullah Canbaz

We investigate the presence of cognitive biases in three large language models (LLMs): GPT-4o, Gemma 2, and Llama 3.1. The study uses 1,500 experiments across nine established cogn…

cs.CR2025

Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity

Hao Jian Huang, Hakan T. Otal, M. Abdullah Canbaz

This paper presents the design and implementation of a Federated Learning (FL) testbed, focusing on its application in cybersecurity and evaluating its resilience against poisoning…

cs.CL2025

LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice

M. Mikail Demir, Hakan T. Otal, M. Abdullah Canbaz

Large Language Models (LLMs) hold promise for advancing legal practice by automating complex tasks and improving access to justice. However, their adoption is limited by concerns o…