Papers Misconceptions
“Misconceptions” 태그가 달린 논문 161편 · 필터 해제
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
Oversmoothing has been recognized as a main obstacle to building deep Graph Neural Networks (GNNs), limiting the performance. This position paper argues that the influence of oversmoothing has been overstated and advocat…
MisconceptionsA Structured Unplugged Approach for Foundational AI Literacy in Primary Education
Younger generations are growing up in a world increasingly shaped by intelligent technologies, making early AI literacy crucial for developing the skills to critically understand and navigate them. However, education in …
Logical ReasoningMisconceptionsNavigateWhen AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research
Recent advances in large language models (LLMs) have fueled the vision of automated scientific discovery, often called AI Co-Scientists. To date, prior work casts these systems as generative co-authors responsible for cr…
Misconceptionsscientific discoveryAutomated Identification of Logical Errors in Programs: Advancing Scalable Analysis of Student Misconceptions
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide …
MisconceptionsHumans can learn to detect AI-generated texts, or at least learn when they can't
This study investigates whether individuals can learn to accurately discriminate between human-written and AI-produced texts when provided with immediate feedback, and if they can use this feedback to recalibrate their s…
MisconceptionsHarnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractors
Generating high-quality MCQs, especially those targeting diverse cognitive levels and incorporating common misconceptions into distractor design, is time-consuming and expertise-intensive, making manual creation impracti…
High School PhysicsMisconceptionsMultiple-choiceQuestion Generation+2Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
Personalized recommendation is widely used in the web applications, and graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems, primarily due to its ability to extract self-super…
Collaborative FilteringContrastive LearningData AugmentationMisconceptions+1LLM Library Learning Fails: A LEGO-Prover Case Study
Recent advancements in the coding, reasoning, and tool-using abilities of LLMs have spurred interest in library learning (i.e., online learning through the creation, storage, and retrieval of reusable and composable func…
Mathematical ReasoningMisconceptionsWhat is AI, what is it not, how we use it in physics and how it impacts... you
Artificial Intelligence (AI) and Machine Learning (ML) have been prevalent in particle physics for over three decades, shaping many aspects of High Energy Physics (HEP) analyses. As AI's influence grows, it is essential …
Anomaly DetectionMisconceptionsFrom Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions
Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accuracy and helping students become independent critical thinkers. In this study, we…
MisconceptionsClarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review
Background Advances in machine learning (ML) models have increased the capability of researchers to detect vaccine hesitancy in social media using Natural Language Processing (NLP). A considerable volume of research has …
MisconceptionsSentiment AnalysisStance DetectionHow to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation
As Large Language Models (LLMs) are widely deployed in diverse scenarios, the extent to which they could tacitly spread misinformation emerges as a critical safety concern. Current research primarily evaluates LLMs on ex…
counterfactualMisconceptionsMisinformationRAGPaths and Ambient Spaces in Neural Loss Landscapes
Understanding the structure of neural network loss surfaces, particularly the emergence of low-loss tunnels, is critical for advancing neural network theory and practice. In this paper, we propose a novel approach to dir…
MisconceptionsEmergent Abilities in Large Language Models: A Survey
Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial general intelligence. The scaling of these models, accomplished by increasing the nu…
In-Context LearningMisconceptionsSurveyAnalyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems
Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This…
MisconceptionsThe Imitation Game for Educational AI
As artificial intelligence systems become increasingly prevalent in education, a fundamental challenge emerges: how can we verify if an AI truly understands how students think and reason? Traditional evaluation methods l…
Distractor GenerationMisconceptionsRetrieval-augmented systems can be dangerous medical communicators
Patients have long sought health information online, and increasingly, they are turning to generative AI to answer their health-related queries. Given the high stakes of the medical domain, techniques like retrieval-augm…
MisconceptionsRetrievalRetrieval-augmented GenerationFoundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact
From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-v…
MisconceptionsKnowledge Tracing in Programming Education Integrating Students' Questions
Knowledge tracing (KT) in programming education presents unique challenges due to the complexity of coding tasks and the diverse methods students use to solve problems. Although students' questions often contain valuable…
Knowledge TracingMisconceptionsGenerating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
In designing multiple-choice questions (MCQs) in education, creating plausible distractors is crucial for identifying students' misconceptions and gaps in knowledge and accurately assessing their understanding. However, …
Distractor GenerationMisconceptionsMultiple-choice