Misconceptions
36 papers with code • 1 benchmarks • 1 datasets
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
Benchmarks
These leaderboards are used to track progress in Misconceptions
| Trend | Dataset | Best Model | Paper | Code | Compare |
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BIG-bench
Most implemented papers
Community detection in networks: A user guide
Community detection in networks is one of the most popular topics of modern network science.
Laplace Redux -- Effortless Bayesian Deep Learning
AlexImmer/Laplace
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NeurIPS 2021
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection.
Factuality Enhanced Language Models for Open-Ended Text Generation
nayeon7lee/factualityprompt
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•
In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation.
Design Challenges and Misconceptions in Neural Sequence Labeling
jiesutd/NCRFpp
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•
COLING 2018
We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i. e. NER, Chunking, and POS tagging).
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
uds-lsv/bert-stable-fine-tuning
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ICLR 2021
Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks.
TruthfulQA: Measuring How Models Mimic Human Falsehoods
sylinrl/truthfulqa
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ACL 2022
We crafted questions that some humans would answer falsely due to a false belief or misconception.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
Parting with Misconceptions about Learning-based Vehicle Motion Planning
autonomousvision/nuplan_garage
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•
The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting.
A Variational Inequality Perspective on Generative Adversarial Networks
GauthierGidel/Variational-Inequality-GAN
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ICLR 2019
Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train.
