SOTAVerified

Misconceptions

Measures whether a model can discern popular misconceptions from the truth.

Example:

        input: The daddy longlegs spider is the most venomous spider in the world.
        choice: T
        choice: F
        answer: F

        input: Karl Benz is correctly credited with the invention of the first modern automobile.
        choice: T
        choice: F
        answer: T

Source: BIG-bench

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 161 papers

TitleStatusHype
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction—0
Generative AI in Education: From Foundational Insights to the Socratic Playground for Learning—0
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems—0
Toward Semi-Automatic Misconception Discovery Using Code Embeddings—0
LLM-based Cognitive Models of Students with Misconceptions—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
How Useful are Gradients for OOD Detection Really?—0
Human-centered trust framework: An HCI perspective—0
Humans can learn to detect AI-generated texts, or at least learn when they can't—0
Identifying science concepts and student misconceptions in an interactive essay writing tutor—0
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank—0
Biometric recognition: why not massively adopted yet?—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing—0
Justices for Information Bottleneck Theory—0
Knowledge Tracing in Programming Education Integrating Students' Questions—0
Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts—0
Laplace Redux - Effortless Bayesian Deep Learning—0
A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation—0
Learnable: Theory vs Applications—0
A clarification of misconceptions, myths and desired status of artificial intelligence—0
Limitations of Deep Neural Networks: a discussion of G. Marcus' critical appraisal of deep learning—0
Listening to Patients: A Framework of Detecting and Mitigating Patient Misreport for Medical Dialogue Generation—0
LLM Library Learning Fails: A LEGO-Prover Case Study—0
Machine Learning Students Overfit to Overfitting—0
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