Search Results for author:
Found 10 papers, 9 papers with code
TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models
Our study of existing benchmarks shows that this capability of MFMs is likely overestimated as many questions can be solved by using a single, few, or out-of-order frames.
A Universal Discriminator for Zero-Shot Generalization
In the finetuning setting, our approach also achieves new state-of-the-art results on a wide range of NLP tasks, with only 1/4 parameters of previous methods.
Zero-Label Prompt Selection
Natural language prompts have been shown to facilitate cross-task generalization for large language models.
Prompt-Based Metric Learning for Few-Shot NER
Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples.
On the Performance of Data Compression in Clustered Fog Radio Access Networks
The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency.
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train.
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
The few-shot natural language understanding (NLU) task has attracted much recent attention.
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
Most previous methods for text data augmentation are limited to simple tasks and weak baselines.
GPT Understands, Too
Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU).
CPM: A Large-scale Generative Chinese Pre-trained Language Model
However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
