Prompt-Based Learning

Updated 26 October 2025

Prompt-based learning is a paradigm where large pretrained models are adapted via carefully crafted textual or continuous prompts rather than gradient-based fine-tuning.
It leverages in-context learning with few-shot examples to bridge the gap between pretraining tasks and downstream applications in language, vision, and multimodal tasks.
This method enhances efficiency by reducing compute demands while aligning pretraining and task objectives, though it faces vulnerabilities like adversarial triggers.

Prompt-based learning is a paradigm in which the adaptation of a large pretrained model to downstream tasks is achieved not through gradient-based fine-tuning of weights, but rather by providing carefully engineered textual or continuous prompts as part of the input. The model is "prompted" with verbalized task instructions and, in many modern variants, a handful of example input–output pairs. This approach aligns the prediction objective for downstream tasks with the pretraining objective (e.g., masked language modeling or causal language modeling) and has shown exceptional performance in few-shot and zero-shot scenarios, especially for LLMs and increasingly for vision and multi-modal models. Prompt-based learning modifies the input distribution to "nudge" the model toward the desired behavior, leveraging the pretrained model’s generalization capability without requiring additional gradient updates to the model parameters.

1. Foundations and Paradigms of Prompt-Based Learning

Prompt-based learning reframes downstream tasks to more closely mimic the format of the pretraining tasks encountered by foundation models, such as autoregressive or masked token prediction. For instance, a conventional classification task ("Is this review positive?") is rewritten as a cloze-style statement ("This movie was <mask>"), with the model predicting the masked token. This reformulation bridges the gap between pretraining and downstream adaptation without architectural changes or additional output heads. Two primary forms of prompts are used:

Discrete/textual prompts: Explicitly phrased text concatenated with original inputs; these can be manually crafted or optimized ("soft prompts").
Continuous/soft prompts: Learned embeddings that are prepended or interleaved with token embeddings; these are parameter-efficient and do not correspond to real tokens.

Prompts can be constructed to facilitate in-context learning by including few-shot input–output examples within the prompt ((Madotto et al., 2021); (Ding et al., 2021)). This supports zero-, one-, and k-shot generalization, where the prompt provides demonstrations of the desired task.

2. Core Methodologies and Prompt Design Strategies

Prompt-based learning relies on three interacting components:

Prompt Template: Defines how the prompt is constructed from the original input, optionally with extra metadata or contextual examples. Templates may be manually designed templates, learned token sequences, or automatically generated from data ((Ding et al., 2021); (Zhou et al., 2023)).
Verbalizer: Maps model outputs (typically from the pretraining vocabulary) to target task-specific labels. Manual verbalizers assign candidate output words to each class; automatic or "prototypical" verbalizers learn this mapping via contrastive objectives (Zhou et al., 2023).
Prompt Model Integration: The prompt-augmented input is passed into the frozen model, and the output at the mask position (or autoregressive continuation) is interpreted according to the prompt's format.

Design strategies include:

Manual prompt engineering: Direct template writing, effective but labor intensive and potentially brittle.
Prompt ensembling: Using a pool of prompts with outputs combined for robustness (Ding et al., 2021).
Learned "soft" prompts: Embedding-level tokens initialized randomly or from real tokens, optimized via supervision or pseudo-labeling.
Automated prompt and verbalizer search: Gradient-based or data-driven search over prompt templates and verbalizer candidates (Zhou et al., 2023).
Dynamic prompt selection: Choosing or modulating prompts based on the input context ((Madotto et al., 2021); (Feng et al., 27 Sep 2024); (Tu et al., 22 Jan 2025)).

3. Applications Across NLP, Vision, and Multimodal Tasks

Prompt-based learning has demonstrated efficacy in a diverse range of domains:

Language: Text classification (sentiment analysis, relation extraction (Zhang et al., 2023)), question answering, text generation (response and dialogue systems (Madotto et al., 2021); (Su et al., 2022)), named entity recognition, and knowledge probing.
Vision: Few-shot and zero-shot classification, image captioning (Zhu et al., 2022), and object recognition via visual prompt tuning or pooled prompts ((Zhang et al., 2022); (Jiang et al., 12 Jul 2025)).
Multimodal: Tasks involving both vision and language such as vision-language retrieval, visual question answering, and unpaired image captioning (Zhu et al., 2022).
Continual Learning: Prompt-based continual learning techniques exhibit robust performance under sequential task learning, leveraging prompt pools, prompt selection strategies, and mixture-of-expert mechanisms to mitigate catastrophic forgetting and improve memory efficiency ((Kim et al., 25 Feb 2024); (Feng et al., 27 Sep 2024); (Tu et al., 22 Jan 2025); (Le et al., 29 Sep 2025)).

4. Model Performance, Transferability, and Resource Considerations

Prompt-based learning with large models (e.g., GPT-J-6B (Madotto et al., 2021)) can achieve performance competitive with or even superior to fully fine-tuned models on a range of tasks, particularly as model size increases and prompt design is optimized. Performance metrics such as accuracy, F1, BLEU, Rouge-L, and perplexity are task-dependent but consistently show minimal degradation (<1%), and in some cases improvement, relative to state-of-the-art fine-tuned baselines.

A crucial advantage is resource efficiency. Few-shot prompt tuning is training-free or requires tuning only a small parameter subset (prompt tokens or verbalizers), significantly reducing storage and compute requirements. Prompt-based methods scale to new tasks by simply updating or adding prompts. Trade-offs include context-length limitations, sensitivity to prompt construction and ordering, and, without tuning, potential underperformance on highly structured outputs or data requiring task-specific world knowledge ((Madotto et al., 2021); (Kang et al., 2 Mar 2025)).

Prompt selection accuracy significantly affects performance in continual learning. Novel mechanisms such as Multi-Query Multi-Key matching (Tu et al., 22 Jan 2025) or sparse mixture-of-expert designs (Le et al., 29 Sep 2025) mitigate prompt selection errors and support scalable, efficient adaptation across many tasks and input distributions.

5. Robustness, Vulnerabilities, and Security Implications

Prompt-based systems have been shown to inherit vulnerabilities from pretraining, most notably susceptibility to adversarial or backdoor triggers (Xu et al., 2022). Triggers inserted in input texts (or in some cases injected during pretraining) can manipulate predictions, with backdoor attacks achieving near 100% attack success rates while maintaining high clean accuracy. These vulnerabilities are universal: adversarial triggers identified for one PLM often transfer across other models and tasks.

Mitigation strategies such as outlier filtering (removing unlikely tokens based on perplexity) reduce adversarial impact but are less effective against backdoor attacks. Conventional classifier fine-tuning (using a <cls> head) is less susceptible than prompt-based classifiers, highlighting the need for further research into robust prompt construction and defense mechanisms in safety-critical applications.

6. Advances in Prompt Selection, Continual Learning, and Scalability

Recent methodologies have focused on addressing the limitations of prompt-based learning in lifelong learning scenarios:

Dynamic Prompt Growing and Selection: Methods such as LW2G (Feng et al., 27 Sep 2024) use task dissimilarity metrics (Hinder Forward Capability, HFC) to determine whether to allocate new prompts or reuse existing ones, balancing prompt pool efficiency with knowledge sharing.
Query-Pool Efficiency: One-stage prompt-based continual learning (Kim et al., 25 Feb 2024) removes redundant query networks and uses intermediate backbone embeddings to reduce computational cost by 50% with marginal accuracy loss; an additional Query-Pool Regularization (QR) aligns prompt selection decisions with the best-performing deep representations.
Sparse Mixture-of-Experts: SMoPE (Le et al., 29 Sep 2025) arranges prompt experts within a shared, sparse MoE architecture—only a subset of prompt experts is activated per input. This approach combines parameter efficiency with dynamic task-specialized adaptation, yielding both high accuracy and low memory usage in large-scale continual learning.
Matching Mechanisms: MQMK (Tu et al., 22 Jan 2025) improves prompt selection accuracy by performing breadth (across task-specific queries) and depth (via class-level keys) searches over the prompt pool, boosting matching rates and average accuracy in challenging incremental learning scenarios.

7. Future Directions and Open Problems

Extending prompt-based learning requires advances in several dimensions:

Scaling Prompt Learning: Fully automated prompt and verbalizer search, greater support for non-English languages, and transfer strategies for multi-domain and cross-lingual settings.
Robustness: Formal analysis of vulnerabilities and the development of more effective, model-agnostic defenses against adversarial and backdoor attacks.
Adaptation and Generalization: Approaches such as prompt diffusion (Du et al., 26 Oct 2024) employ generative models to create sample-specific prompts, offering a pathway to improved robustness under distribution shift and domain adaptation.
Continual Learning and Lifelong Adaptation: Balancing memory, sample efficiency, and performance with scalable, compositional, and dynamically growing or shrinking prompt pools; exploring hybrid architectures combining prompt tuning with limited backbone fine-tuning.
Cross-modality Integration: Improved methods for integrating prompts across modalities (text, vision, audio) and establishing unified frameworks to support a broad range of tasks and input types.

Prompt-based learning thus represents a flexible, modular, and efficient approach for the adaptation of large pretrained models. Its continued development is likely to focus on improved automation, robustness, and adaptability across dynamic task distributions and real-world application domains, guided by theoretical understanding of prompt construction, selection, and model interaction dynamics.