Misconceptions
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Benchmarks
BIG-bench
Most implemented
Community detection in networks: A user guide
Laplace Redux -- Effortless Bayesian Deep Learning
Factuality Enhanced Language Models for Open-Ended Text Generation
Parting with Misconceptions about Learning-based Vehicle Motion Planning
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Papers
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+2