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Misconceptions
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Structured Unplugged Approach for Foundational AI Literacy in Primary Education
When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research
Automated Identification of Logical Errors in Programs: Advancing Scalable Analysis of Student Misconceptions
Humans can learn to detect AI-generated texts, or at least learn when they can't
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractors
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
LLM Library Learning Fails: A LEGO-Prover Case Study
What is AI, what is it not, how we use it in physics and how it impacts... you
From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions
Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation
Paths and Ambient Spaces in Neural Loss Landscapes
Emergent Abilities in Large Language Models: A Survey
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems
The Imitation Game for Educational AI
Retrieval-augmented systems can be dangerous medical communicators
Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact
Knowledge Tracing in Programming Education Integrating Students' Questions
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
Generative AI in Education: From Foundational Insights to the Socratic Playground for Learning
Decoding Knowledge in Large Language Models: A Framework for Categorization and Comprehension
A Graphical Approach to State Variable Selection in Off-policy Learning
Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation
Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development
Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology
A Study on Characterization of Near-Field Sub-Regions For Phased-Array Antennas
LLM-based Cognitive Models of Students with Misconceptions
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts
Listening to Patients: A Framework of Detecting and Mitigating Patient Misreport for Medical Dialogue Generation
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills
A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product
Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs
Enhancing Knowledge Tracing with Concept Map and Response Disentanglement
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