most citedCould ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants

36 citations · 40 across the 11 of their papers we have counts for

collaborators

11 papers

cs.LG2025★ 1 cited

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data

Ljubomir Rokvic, Panayiotis Danassis, Boi Faltings

In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for exa…

cs.CL2025

Unraveling Misinformation Propagation in LLM Reasoning

Yiyang Feng, Yichen Wang, Shaobo Cui +3

Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, positioning them as promising tools for supporting human problem-solving. However, what happens…

cs.DC2025

FedCDC: A Collaborative Framework for Data Consumers in Federated Learning Market

Zhuan Shi, Patrick Ohl, Boi Faltings

Federated learning (FL) allows machine learning models to be trained on distributed datasets without directly accessing local data. In FL markets, numerous Data Consumers compete t…

cs.CV2025

CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models

Shunchang Liu, Zhuan Shi, Lingjuan Lyu +2

Assessing whether AI-generated images are substantially similar to source works is a crucial step in resolving copyright disputes. In this paper, we propose CopyJudge, a novel auto…

cs.LG2024

Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning

Zhuan Shi, Yifei Song, Xiaoli Tang +2

Generative art using Diffusion models has achieved remarkable performance in image generation and text-to-image tasks. However, the increasing demand for training data in generativ…

cs.CL2024

Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

Shaobo Cui, Junyou Li, Luca Mouchel +2

To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating inte…

cs.CY2024

RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model

Zhuan Shi, Jing Yan, Xiaoli Tang +2

The increasing sophistication of text-to-image generative models has led to complex challenges in defining and enforcing copyright infringement criteria and protection. Existing me…

cs.CY2024★ 36 cited

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants

Beatriz Borges, Negar Foroutan, Deniz Bayazit +87

AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…

cs.CL2024

A Logical Fallacy-Informed Framework for Argument Generation

Luca Mouchel, Debjit Paul, Shaobo Cui +3

Despite the remarkable performance of Large Language Models (LLMs) in natural language processing tasks, they still struggle with generating logically sound arguments, resulting in…

cs.CL2024

The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning

Shaobo Cui, Zhijing Jin, Bernhard Schölkopf +1

Understanding commonsense causality is a unique mark of intelligence for humans. It helps people understand the principles of the real world better and benefits the decision-making…

cs.CL2024★ 3 cited

Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning

Debjit Paul, Robert West, Antoine Bosselut +1

Large language models (LLMs) have been shown to perform better when asked to reason step-by-step before answering a question. However, it is unclear to what degree the model's fina…