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Found 12 papers, 9 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?
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.
Knowledge Unlearning for Mitigating Privacy Risks in Language Models
Pretrained Language Models (LMs) memorize a vast amount of knowledge during initial pretraining, including information that may violate the privacy of personal lives and identities.
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.
Prompt Injection: Parameterization of Fixed Inputs
Through these explorations, we show that PI can be a promising direction for conditioning language models, especially in scenarios with long and fixed prompts.
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Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called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.
Learning to Balance with Incremental Learning
Classification tasks require balanced distribution of data in order to ensure the learner to be trained to generalize over all classes.
Sequential Targeting: an incremental learning approach for data imbalance in text classification
Classification tasks require a balanced distribution of data to ensure the learner to be trained to generalize over all classes.
