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Found 6 papers, 5 papers with code
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Large Language Models (LLMs) have shown enhanced capabilities of solving novel tasks by reasoning step-by-step known as Chain-of-Thought (CoT) reasoning; how can we instill the same capability of reasoning step-by-step on unseen tasks into LMs that possess less than <100B parameters?
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance.
Retrieval of Soft Prompt Enhances Zero-Shot Task Generalization
During zero-shot inference with language models (LMs), using hard prompts alone may not be able to fully describe the target task.
Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation
We compared the performance between the auto-detection system and the human eye.
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks
In our network, we utilize an average ensemble among multiple parts as a final feature to capture various temporal dependencies.
Ranked #86 on
Skeleton Based Action Recognition
on NTU RGB+D
