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 1–25 of 161 papers

TitleStatusHype
Training Compute-Optimal Large Language ModelsCode6
Factuality Enhanced Language Models for Open-Ended Text GenerationCode5
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
The pitfalls of next-token predictionCode2
Parting with Misconceptions about Learning-based Vehicle Motion PlanningCode2
Towards Democratizing Joint-Embedding Self-Supervised LearningCode2
Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational NeedsCode1
TruthfulQA: Measuring How Models Mimic Human FalsehoodsCode1
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative FilteringCode1
Re-Examining Linear Embeddings for High-Dimensional Bayesian OptimizationCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
A Tutorial on VAEs: From Bayes' Rule to Lossless CompressionCode1
Noise-powered Multi-modal Knowledge Graph Representation FrameworkCode1
Improving the Validity of Automatically Generated Feedback via Reinforcement LearningCode1
Enhancing Knowledge Tracing with Concept Map and Response DisentanglementCode1
Emergent Communication under CompetitionCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMsCode1
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong BaselinesCode1
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems—0
Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology—0
A clarification of misconceptions, myths and desired status of artificial intelligence—0
Challenges and Trends in User Trust Discourse in AI—0
A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product—0
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