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Found 10 papers, 7 papers with code
HypMix: Hyperbolic Interpolative Data Augmentation
Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.
CIAug: Equipping Interpolative Augmentation with Curriculum Learning
CIAug achieves state-of-the-art results over existing interpolative augmentation methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks.
DMix: Distance Constrained Interpolative Mixup
Interpolation-based regularisation methods have proven to be effective for various tasks and modalities.
Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging
There have been efforts in cross-lingual transfer learning for various tasks.
DMix: Adaptive Distance-aware Interpolative Mixup
Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space.
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers.
Self-Influence Guided Data Reweighting for Language Model Pre-training
Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training.
Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications
Large-scale pre-trained language models have shown outstanding performance in a variety of NLP tasks.
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations
Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios.
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization
We also introduce a number of state-of-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images.
