Draft:Zero-shot prompting
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Zero-shot prompting is a technique used in natural language processing (NLP) where a language model performs tasks it hasn’t been explicitly trained for, without receiving task-specific examples or labels during its training phase.[1][2] This method allows a model to generate responses, complete tasks, or answer questions based on general knowledge acquired from large-scale text corpora.[3]
Overview
Zero-shot prompting involves providing a language model with a plain language prompt or instruction and expecting the model to understand and perform a task such as translation, classification, summarization, or question answering without having been fine-tuned on that specific task. The technique relies on the model's ability to generalize from its training data.[1]
Key Concepts
Generalization
Zero-shot prompting leverages the model's capacity for generalization—the ability to apply prior knowledge to unfamiliar tasks or domains. This occurs because the model has been pretrained on massive datasets, which allows it to recognize patterns, facts, and relationships between words. This generalization enables the model to handle new tasks without explicit training on them.
Pretrained Language Models
Language models like GPT and BERT are pretrained on vast amounts of text data. During zero-shot prompting, users tap into this general understanding of language to perform tasks that may not have been explicitly included during the model's fine-tuning phase. The model is able to perform these tasks based on the knowledge it has "learned" during pretraining.
Instruction Following
A significant aspect of zero-shot prompting is the model's ability to follow instructions. The model is given a prompt that includes explicit instructions for the task it is expected to perform. The quality of the model's output often depends on the clarity and specificity of the prompt. Vague or unclear prompts can lead to suboptimal results.
Applications
Zero-shot prompting is used across a variety of tasks in NLP, including:
It is particularly useful in scenarios where labeled data is scarce or unavailable, as the model can perform tasks without the need for retraining or fine-tuning for each specific task.
Comparison with Few-shot Learning
Zero-shot prompting differs from few-shot learning in that it does not involve providing the model with any task-specific examples. In few-shot learning, a model is given a few examples of the task during training or prompting, which helps guide its output. In contrast, zero-shot prompting relies entirely on the model's pretrained knowledge and its ability to generalize to new tasks. While zero-shot learning offers versatility, it can be less reliable than few-shot learning in certain cases where the task requires detailed or specialized understanding.[4]
See Also
- Natural language processing
- Few-shot learning
- Machine learning
- Transformer (machine learning model)
- GPT (language model)
- BERT (language model)
References
- ^ 1.0 1.1 Radford, Alec; Wu, Jeffrey; Child, Rewon; Luan, David; Amodei, Dario; Sutskever, Ilya (2019). "Language Models are Unsupervised Multitask Learners" (PDF). OpenAI.
We demonstrate language models can perform down-stream tasks in a zero-shot setting – without any parameter or architecture modification
- ^ Zdrok, Oksana (2024-03-19). "7 Contrasts Between Zero-Shot and Few-Shot Prompting". Shelf. Retrieved 2024-09-21.
- ^ "What Are Large Language Models (LLMs)? | IBM". www.ibm.com. 2023-11-02. Retrieved 2024-09-20.
- ^ Sivarajkumar, Sonish; Kelley, Mark; Samolyk-Mazzanti, Alyssa; Visweswaran, Shyam; Wang, Yanshan (2023-09-14). "An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing". arXiv:2309.08008.
- Brown, Tom et al. (2020). "Language Models are Few-Shot Learners." ArXiv. [1]
- Raffel, Colin et al. (2020). "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer." Journal of Machine Learning Research 21(140), pp. 1-67. [2]
- Vaswani, Ashish et al. (2017). "Attention is All You Need." NeurIPS. [3]