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 51–75 of 161 papers

TitleStatusHype
Listening to Patients: A Framework of Detecting and Mitigating Patient Misreport for Medical Dialogue Generation—0
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills—0
A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product—0
Classifier-Free Guidance is a Predictor-Corrector—0
Problems in AI, their roots in philosophy, and implications for science and society—0
Neural topology optimization: the good, the bad, and the ugly—0
When big data actually are low-rank, or entrywise approximation of certain function-generated matricesCode0
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in EducationCode0
Formalising Anti-Discrimination Law in Automated Decision Systems—0
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice QuestionsCode0
Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language LearningCode0
Refining Skewed Perceptions in Vision-Language Models through Visual Representations—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
Toward In-Context Teaching: Adapting Examples to Students' Misconceptions—0
Common pitfalls to avoid while using multiobjective optimization in machine learning—0
Can a Hallucinating Model help in Reducing Human "Hallucination"?—0
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration—0
A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation PracticeCode0
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning—0
Differential contributions of machine learning and statistical analysis to language and cognitive sciences—0
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank—0
Axiomatic modeling of fixed proportion technologies—0
Breaking Boundaries: A Chronology with Future Directions of Women in Exercise Physiology Research, Centred on Pregnancy—0
SoK: On Gradient Leakage in Federated Learning—0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
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