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Found 16 papers, 10 papers with code
Online Adaptation of Language Models with a Memory of Amortized Contexts
We propose an amortized feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank.
SelectLLM: Can LLMs Select Important Instructions to Annotate?
However, how to select unlabelled instructions is not well-explored, especially in the context of LLMs.
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
This work delves into the expanding role of large language models (LLMs) in generating artificial data.
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice.
Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning
In this paper, we investigate task-specific preferences between pairs of input texts as a new alternative way for such auxiliary data annotation.
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information
However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy.
Annotation Imputation to Individualize Predictions: Initial Studies on Distribution Dynamics and Model Predictions
In our analysis of the results, we found that the choice of imputation method significantly impacts soft label changes and distribution.
Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information
Particularly, we extract disagreement labels from the annotators' voting histories in the five subjective datasets, and then fine-tune language models to predict annotators' disagreement.
Time Is MattEr: Temporal Self-supervision for Video Transformers
Understanding temporal dynamics of video is an essential aspect of learning better video representations.
Patch-level Representation Learning for Self-supervised Vision Transformers
Despite its simplicity, we demonstrate that it can significantly improve the performance of existing SSL methods for various visual tasks, including object detection and semantic segmentation.
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes.
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different
Remarkably, our method is more effective on the challenging low-data and class-imbalanced regimes, and the learned augmentation policy is well-transferable to the different tasks and models.
PASS: Patch-Aware Self-Supervision for Vision Transformer
This paper aims to improve their performance further by utilizing the architectural advantages of the underlying neural network, as the current state-of-the-art visual pretext tasks for self-supervised learning do not enjoy the benefit, i. e., they are architecture-agnostic.
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled data when labeled data is scarce, the existing SSL algorithms typically assume that training class distributions are balanced.
M2m: Imbalanced Classification via Major-to-minor Translation
In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion.
Ranked #43 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Simplified Stochastic Feedforward Neural Networks
In this paper, we aim at developing efficient training methods for SFNN, in particular using known architectures and pre-trained parameters of DNN.
