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 101–150 of 161 papers

TitleStatusHype
Challenges and Trends in User Trust Discourse in AI—0
On the lifting and reconstruction of nonlinear systems with multiple invariant sets—0
Demystifying Misconceptions in Social Bots Research—0
Succinct Representations for Concepts—0
Response to Moffat's Comment on "Towards Meaningful Statements in IR Evaluation: Mapping Evaluation Measures to Interval Scales"—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
The Monitor Model and its Misconceptions: A Clarification—0
Machine Learning Students Overfit to Overfitting—0
Dynamics and triggers of misinformation on vaccines—0
Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)—0
How Useful are Gradients for OOD Detection Really?—0
A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation TopicsCode0
From Solution Synthesis to Student Attempt Synthesis for Block-Based Visual Programming TasksCode0
Rectified Max-Value Entropy Search for Bayesian Optimization—0
Biometric recognition: why not massively adopted yet?—0
Metagenomic Analysis using Phylogenetic Placement -- A Review of the First Decade—0
Resolving conceptual issues in Modern Coexistence TheoryCode0
Big Data is not the New Oil: Common Misconceptions about Population Data—0
Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox & Causal Artificial Intelligence—0
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment AnalysisCode0
The kernel perspective on dynamic mode decomposition—0
Laplace Redux - Effortless Bayesian Deep Learning—0
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing—0
Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networksCode0
Knowledge, beliefs, attitudes and perceived risk about COVID-19 vaccine and determinants of COVID-19 vaccine acceptance in Bangladesh—0
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems—0
Toward Semi-Automatic Misconception Discovery Using Code Embeddings—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
Limitations of Deep Neural Networks: a discussion of G. Marcus' critical appraisal of deep learning—0
Hindsight and Sequential Rationality of Correlated PlayCode0
Quantum Technology for Economists—0
COVIDLies: Detecting COVID-19 Misinformation on Social Media—0
Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model—0
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability—0
Collecting the Public Perception of AI and Robot RightsCode0
A clarification of misconceptions, myths and desired status of artificial intelligence—0
Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge—0
The Bussgang Decomposition of Non-Linear Systems: Basic Theory and MIMO Extensions—0
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe—0
Crowdsourcing the Perception of Machine Teaching—0
Deep Curvature SuiteCode0
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess HypothesesCode0
From Random to Regular: Variation in the Patterning of Retinal Mosaics—0
Discounted Reinforcement Learning Is Not an Optimization Problem—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation—0
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some MisconceptionsCode0
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning InferenceCode0
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering—0
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