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Found 5 papers, 4 papers with code
On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons
Additionally, we tamper with less than 1% of the total neurons in each model during inference and demonstrate that tampering with a few language-specific neurons drastically changes the probability of target language occurrence in text generation.
Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text
While Large Language Models (LLMs) have achieved remarkable performance in many tasks, much about their inner workings remains unclear.
Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment
Vision Transformer (ViT) is becoming more popular in image processing.
Large Language Models are Zero-Shot Reasoners
Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars.
Ranked #1 on
Arithmetic Reasoning
on MultiArith
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training
In this paper, we propose a GAN model that aims to improve the approach to generating diverse texts conditioned by the latent space.
