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Found 11 papers, 10 papers with code
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Using the finding, we develop several variants of MI and increases the effectiveness of the best prompt selection method from 87. 79% to 94. 98%, measured as the ratio of the performance of the selected prompt to that of the optimal oracle prompt.
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?
In-Context Instruction Learning
Instruction learning of Large Language Models (LLMs) has enabled zero-shot task generalization.
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.
Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts
Previous work has shown that there exists a scaling law between the size of Language Models (LMs) and their zero-shot performance on different downstream NLP tasks.
TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models
Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.
Towards Continual Knowledge Learning of Language Models
By highlighting the critical causes of knowledge forgetting, we show that CKL is a challenging and important problem that helps us better understand and train ever-changing LMs.
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning
We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning.
Dimensional Emotion Detection from Categorical Emotion
We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations.
