Prompt-Based Zero-Shot Embedding Extraction

• Prompt-based zero-shot embedding extraction is a technique that uses engineered prompts to extract task-specific embeddings from pretrained models without gradient-based adaptation.
• It employs strategies like manual prompt engineering, meta-prompting, and retrieval-augmented prompt expansion to tailor embeddings for language, vision, and graph tasks.
• Empirical evaluations reveal that these methods achieve competitive or state-of-the-art performance in zero-shot classification, retrieval, and multimodal understanding.

Prompt-based zero-shot embedding extraction is a rapidly developing paradigm in which pretrained models are induced—via prompt engineering, meta-prompting, learned template construction, or prompt exploration—to produce discriminative embeddings for novel tasks or categories with minimal or no labeled data. This approach eschews gradient-based adaptation during inference, instead leveraging the representational structure learned by LLMs, vision-LLMs (VLMs), or contrastively trained encoders, by crafting prompts that elicit domain-relevant feature vectors. Prompt-based zero-shot embedding extraction forms the foundation for universal zero-shot classification, retrieval, multimodal understanding, and knowledge transfer across diverse domains.

1. Foundations of Prompt-based Zero-Shot Embedding Methods

Prompt-based embedding extraction takes a pretrained backbone (language, vision, or multimodal) and manipulates its inputs via carefully designed prompts to obtain, without further supervised tuning, feature representations tailored to new tasks. The theoretical justification derives from the observation that powerful encoders (e.g. those optimized via masked language modeling, contrastive pretraining, or large-scale instruction following) already organize the embedding space so that semantically similar objects are proximate.

Core methodology involves:

• Wrapping the input instance into a prompt (template, instruction, or class description).
• Extracting relevant hidden states as task-specific embeddings, e.g., [MASK] positions for masked LMs (Wei et al., 2022), pooled output for sentence encoders (Hong et al., 2022), or the last token for decoder LLMs (Ju et al., 1 Aug 2025).
• Comparing embeddings of test instances and prompted class/prototype descriptions via cosine similarity, softmax, or contrastive distance, returning zero-shot predictions or enabling retrieval.

Prompt-based methods contrast with pure parametric adaptation, instead relying on input manipulation and the model's latent knowledge.

2. Key Methodological Approaches

A variety of methodological strategies are deployed for prompt-based zero-shot embedding extraction:

• Manual Prompt Engineering and Verbalizers: Early methods mapped classes to natural-language tokens/phrases manually inserted into templates; e.g., “a photo of a {class}” for CLIP-style models or mask-based prompts for MLMs (Wei et al., 2022).
• Prompt Ensembling and Weighting: Pooling multiple prompts—either by averaging embeddings or by learned weighting functions (e.g., bias-corrected scores in ZAPWEIGHT (Allingham et al., 2023))—systematically improves robustness and accuracy over single-template approaches.
• Automatic Prompt Generation (Meta-Prompting): LLMs are meta-prompted to produce diverse, class-specific templates free of human intervention. For instance, MPVR (Mirza et al., 2024) uses a two-stage LLM process to generate and fill prompts for each class, ensembles the resulting embeddings, and achieves superior zero-shot accuracy.
• Retrieval-Augmented Prompt Expansion: Label verbalizers are enhanced with retrieved, semantically similar sentences from large corpora, mitigating the information bottleneck of terse prompts. RaLP (Hong et al., 2022) exemplifies this, extending label prompts to rich pseudo-label sets that anchor embedding comparison.
• Learned or Retrieved Soft Prompts: Tuning and then retrieving soft prompt embeddings based on input/query similarity enables efficient adaptation to new tasks, as in ROSPR (Ye et al., 2022), which augments instruction models with retrieved soft prompt embeddings.
• Prototypical Prompt Verbalizers: Labels are represented by continuous “prototype” embeddings computed from mask-position hidden states averaged over templated, unlabeled sentences (PPV (Wei et al., 2022)), enabling non-discrete, bias-resistant classification.

3. Prompt Engineering in Vision, Multimodal, and Graph Domains

Zero-shot embedding extraction is not confined to language modeling.

• Vision-LLMs: In CLIP-style dual encoders, prompts describing the target class are embedded and compared to image representations for classification or retrieval. Prompt ensembling, automatic weighting, and LLM-guided meta-prompting have all been shown to provide substantial gains (Mirza et al., 2024, Allingham et al., 2023).
• Composed Image Retrieval: The Prompt Directional Vector (PDV) method (Tursun et al., 11 Feb 2025) combines baseline text and image embeddings, computes the semantics of prompt-induced modification as Δ_PDV = E_t′ − E_t, and applies this direction to both modalities. Weighting and scaling of Δ_PDV, followed by fusion, modulates the “strength” of the prompt in retrieval embeddings, significantly outperforming prior static approaches.
• Multimodal Knowledge Graph Embedding: In MMKGs, ZSLLM (Liu et al., 10 Mar 2025) uses class label prompts fed through LLMs to obtain semantic embeddings for unseen entities, projects them into the MMKG space, and fuses them with graph neural features. This pipeline allows entirely unseen categories to receive discriminative, transferable representations.
• Text-attributed Graphs: G-Prompt (Huang et al., 2023) appends prompts and a graph-based adapter atop a PLM, propagating both textual and topological cues into prompt-based node embeddings, yielding interpretability and strong zero-shot performance on large real-world graphs.
• Universal Multimodal Embedding via Hierarchical Prompts: MLLMs can be turned into discriminative embedders by using hierarchical prompts (system-level and representation-level) and extracting the last-token hidden state post-prompting (Ju et al., 1 Aug 2025). This procedure is agnostic to modality, requiring no contrastive retraining, and achieves competitive zero-shot performance.

4. Prompt Selection, Scoring, and Automation

Prompt selection and aggregation play central roles in maximizing zero-shot transfer.

• Bias-Corrected Prompt Scoring: ZAPWEIGHT (Allingham et al., 2023) proposes a debiased calibration-based score for each prompt, correcting for class prior and uniformity biases, and converts scores into softmax weights for ensembling, improving accuracy on ImageNet and fine-grained datasets by 2–4 points.
• LLM-Driven Prompt Generation: Meta-prompting frameworks (MPVR (Mirza et al., 2024)) fully automate template and prompt discovery, eliminating the need for per-class engineering, and generate 300+ class-specific templates per class, which are averaged to yield robust embeddings.
• Soft Prompt Retrieval: Instruct-following models (e.g., T0) can retrieve previously tuned soft prompts that best match the query, using frequency- or format-based selection (ROSPR (Ye et al., 2022)).
• Ablation Analyses: Systematic studies establish that format diversity, aligning answer choices, and averaging in text embedding space are more important for zero-shot generalization than adding new task types or averaging in probability space (Ye et al., 2022, Allingham et al., 2023, Mirza et al., 2024).
• Minimal Prompt Engineering: Both ZSLLM (Liu et al., 10 Mar 2025) and G-Prompt (Huang et al., 2023) report that even basic tokens (class names) can seed useful embedding extraction in robust architectures—additional template design and graph adapters can provide domain-specific boosts.

Prompt Selection Method	Key Principle/Operation	Representative Work
Manual/Template-based	Human-crafted fixed templates	(Wei et al., 2022)
Debiased Weighting	Bias-corrected ensemble weighting	(Allingham et al., 2023)
LLM-powered Meta-Prompting	Automated multi-stage prompt generation	(Mirza et al., 2024)
Retrieval-Augmented Pseudo-Labels	Expand prompts using semantically similar text	(Hong et al., 2022)
Soft-prompt Nearest Retrieval	Nearest neighbor retrieval of tuned soft prompts	(Ye et al., 2022)

5. Performance and Empirical Evaluation

Prompt-based zero-shot embedding extraction methods demonstrate consistently strong or state-of-the-art results across language, vision, graph, and multimodal benchmarks:

• Textual Zero-Shot Classification: RaLP (Hong et al., 2022) outperforms larger autoregressive LMs with nonparametric metric-space matching, reaching up to 79.8% closed-set accuracy on ST5-XXL, rivaling GPT-3-175B.
• Vision-Language Classification: Meta-prompted ensembles (MPVR (Mirza et al., 2024)) yield +3–5% top-1 gains over CLIP’s default prompt, achieving 65% mean accuracy on 20 datasets. Bias-corrected weighted ensembles provide 2–4 point improvements on ImageNet, CUB-200, and other fine-grained sets (Allingham et al., 2023).
• Composed Image Retrieval: PDV (Tursun et al., 11 Feb 2025) delivers +4.5 to +11.6 Recall@k gains on FashionIQ and CIRR benchmarks compared to static embedding baselines, all with ≲5% computational overhead.
• Multimodal and Graph Embedding: ZSLLM (Liu et al., 10 Mar 2025) achieves up to +7–31% absolute Top-1 accuracy gains for unseen classes in MMKGs by fusing LLM-prompted and structural features. G-Prompt (Huang et al., 2023) attains AUC close to fully supervised results with zero-shot templates.
• Universal Multimodal Embedding: Hierarchical-prompted MLLMs (Ju et al., 1 Aug 2025) achieve zero-shot performance competitive with contrastively trained CLIP models and, with self-aware hard negative mining, set state-of-the-art when modestly fine-tuned, demonstrating the power of prompt-centric approaches even for generative decoder stacks.

6. Limitations, Interpretability, and Future Directions

Despite robust empirical successes, several limitations and open questions remain.

• Prompt Design Sensitivity: Complex or ambiguous prompts can over-/under-correct latent representations (PDV (Tursun et al., 11 Feb 2025)); prompt ablations show that system/instructional cues are highly influential (MPVR (Mirza et al., 2024, Ju et al., 1 Aug 2025)).
• Scalability and Cost: Automated prompt generation pipelines may involve hundreds or thousands of LLM queries per label (MPVR (Mirza et al., 2024)); soft prompt retrieval and storage require careful key-value design (Ye et al., 2022).
• Hallucination and Out-of-Distribution: LLM-generated prompts may hallucinate unrealistic attribute detail, and simple class-name prompts may lack discriminative power for OOD categories (Mirza et al., 2024, Liu et al., 10 Mar 2025).
• Interpretability: Some approaches, such as G-Prompt (Huang et al., 2023), enhance interpretability by tying dimensions directly to vocabulary tokens, enabling human inspection. Prototype-based methods enable direct reading of cluster centroids (Wei et al., 2022).
• Modal Extension and Joint Objectives: Extensions to audio, video, or complex multimodal settings are largely open (discussion in (Ju et al., 1 Aug 2025, Mirza et al., 2024)); balancing generative and discriminative training objectives in MLLMs remains an unresolved challenge.
• Prompt Pool Diversity: Empirical evidence supports the value of format diversity/answer choices over mere task diversity in prompt libraries (Ye et al., 2022, Allingham et al., 2023).

7. Applications and Prospects

Prompt-based zero-shot embedding extraction underpins a broad range of zero- and few-shot modeling regimes:

• Fully automated image/vision zero-shot recognition pipelines (Mirza et al., 2024, Allingham et al., 2023);
• Efficient composed image retrieval with tunable prompt strengths (Tursun et al., 11 Feb 2025);
• Open-category visual and structured-component knowledge graph completion (Liu et al., 10 Mar 2025);
• Interpretable node classification in large text-attributed graphs (Huang et al., 2023);
• Universal multimodal embedding without costly contrastive pretraining (Ju et al., 1 Aug 2025).

A plausible implication is that as LLMs and VLMs scale in both capability and coverage, prompt-based extraction (especially when coupled with meta-prompting, retrieval, and bias correction) will become the dominant technique for transferring and deploying universal representations in open-world machine learning scenarios.