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 26–50 of 161 papers

TitleStatusHype
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective DistractorsCode0
LLM Library Learning Fails: A LEGO-Prover Case Study—0
What is AI, what is it not, how we use it in physics and how it impacts... you—0
From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions—0
Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review—0
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit MisinformationCode0
Paths and Ambient Spaces in Neural Loss LandscapesCode0
Emergent Abilities in Large Language Models: A Survey—0
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems—0
The Imitation Game for Educational AI—0
Retrieval-augmented systems can be dangerous medical communicators—0
Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact—0
Knowledge Tracing in Programming Education Integrating Students' Questions—0
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
Decoding Knowledge in Large Language Models: A Framework for Categorization and Comprehension—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation—0
Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning DistractorCode0
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development—0
Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology—0
A Study on Characterization of Near-Field Sub-Regions For Phased-Array Antennas—0
LLM-based Cognitive Models of Students with Misconceptions—0
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models—0
Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts—0
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