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 126–150 of 161 papers

TitleStatusHype
Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning—0
Using language models in the implicit automated assessment of mathematical short answer items—0
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning InferenceCode0
A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation PracticeCode0
A Structured Unplugged Approach for Foundational AI Literacy in Primary EducationCode0
A Variational Inequality Perspective on Generative Adversarial NetworksCode0
A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation TopicsCode0
Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute MisconceptionsCode0
Clarify: Improving Model Robustness With Natural Language CorrectionsCode0
Collecting the Public Perception of AI and Robot RightsCode0
Community detection in networks: A user guideCode0
Design Challenges and Misconceptions in Neural Sequence LabelingCode0
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice QuestionsCode0
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context LearningCode0
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment AnalysisCode0
Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context LearningCode0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
From Solution Synthesis to Student Attempt Synthesis for Block-Based Visual Programming TasksCode0
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective DistractorsCode0
Hindsight and Sequential Rationality of Correlated PlayCode0
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some MisconceptionsCode0
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit MisinformationCode0
Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual ProgrammingCode0
Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning DistractorCode0
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in EducationCode0
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