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Found 12 papers, 4 papers with code
Is GPT-3 a Good Data Annotator?
In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks.
Does GPT-3 Demonstrate Psychopathy? Evaluating Large Language Models from a Psychological Perspective
On the basis of the findings, we recommended the application of more systematic and comprehensive psychological metrics to further evaluate and improve the safety of LLMs.
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.
Generic Cryo-CMOS Device Modeling and EDACompatible Platform for Reliable Cryogenic IC Design
The capability of data processing in quantum computers relies on the CMOS-based cryogenic control and storage systems.
Hierarchical Vectorization for Portrait Images
We evaluate our method on the FFHQR dataset and show that our method is effective for common portrait editing tasks, such as retouching, light editing, color transfer and expression editing.
Flexible Portrait Image Editing with Fine-Grained Control
We develop a new method for portrait image editing, which supports fine-grained editing of geometries, colors, lights and shadows using a single neural network model.
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples.
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER
With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages.
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly.
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks
Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models.
Hierarchical Pointer Net Parsing
Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity.
