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
Reply to Garcia et al.: Common mistakes in measuring frequency dependent word characteristics—0
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems—0
Response to Moffat's Comment on "Towards Meaningful Statements in IR Evaluation: Mapping Evaluation Measures to Interval Scales"—0
Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)—0
Retrieval-augmented systems can be dangerous medical communicators—0
Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review—0
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering—0
SoK: On Gradient Leakage in Federated Learning—0
Clarifying System 1 & 2 through the Common Model of Cognition—0
Succinct Representations for Concepts—0
The Bussgang Decomposition of Non-Linear Systems: Basic Theory and MIMO Extensions—0
The Essential Role of Causality in Foundation World Models for Embodied AI—0
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe—0
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models—0
The Imitation Game for Educational AI—0
Classifier-Free Guidance is a Predictor-Corrector—0
The Monitor Model and its Misconceptions: A Clarification—0
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research—0
The Singularity Controversy, Part I: Lessons Learned and Open Questions: Conclusions from the Battle on the Legitimacy of the Debate—0
Toward In-Context Teaching: Adapting Examples to Students' Misconceptions—0
Towards a Rigorous Analysis of Mutual Information in Contrastive Learning—0
Toward Semi-Automatic Misconception Discovery Using Code Embeddings—0
LLM-based Cognitive Models of Students with Misconceptions—0
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review—0
Understanding the Lexical Simplification Needs of Non-Native Speakers of English—0
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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