LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. We deploy LAMPER in experimental assessments using 128 univariate TS datasets sourced from the UCR archive. Our findings indicate that the feature representation capacity of LAMPER is influenced by the maximum input token threshold imposed by PLMs.
Code (1)
Tasks
Language ModelingLanguage ModellingPrompt EngineeringTime SeriesTime Series ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction
Zero-shot text classification typically relies on prompt engineering, but the inherent prompt brittleness of large language models undermines its reliability. Minor changes in prompt can cause significant discrepancies i…
AttributeLanguage ModelingLanguage ModellingPrompt Engineering+5An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
Large language models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), especially in domains where labeled data is scarce or expensive, such as clinical domain. However, to unlock the clini…
AttributeAttribute ExtractionClinical Knowledgecoreference-resolution+3Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification
This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position. We explor…
Job classificationPrompt Engineeringtext-classificationText Classification+2QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency. However, previous prompt-based…
Few-shot NERNamed Entity RecognitionNamed Entity Recognition (NER)Prompt Engineering+1A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification
Audio-text models trained via contrastive learning offer a practical approach to perform audio classification through natural language prompts, such as "this is a sound of" followed by category names. In this work, we ex…
Audio ClassificationClassificationContrastive LearningPrompt Engineering+1