Zero-Shot Meta-Prompted Qwen-72B Model

Updated 28 October 2025

The paper introduces a zero-shot meta-prompted Qwen-72B model that leverages homoiconic meta-mapping, recursive prompt engineering, and meta-learning for rapid, efficient task adaptation.
This framework unifies task representation and transformation through automated prompt optimization, ensuring robust performance across diverse domains.
Empirical results highlight substantial gains in benchmarks like MATH and GSM8K, demonstrating improved token efficiency and modular reasoning capabilities.

A Zero-Shot Meta-Prompted Qwen-72B Model is a large-scale LLM architecture that leverages meta-prompting and meta-learning methodologies to enable rapid adaptation to entirely novel tasks without task-specific examples. Drawing on foundational, theoretical, and practical advances across zero-shot learning, meta-prompt engineering, and LLM scaling, such a model unifies task representation, transformation, and prompting strategies—resulting in flexible, efficient, and modular reasoning aligned with state-of-the-art performance on complex linguistic, mathematical, and multimodal domains.

1. Homoiconic Meta-Mapping and Latent Task Representations

The homoiconic meta-mapping (HoMM) framework (Lampinen et al., 2019) is central to flexible zero-shot adaptation. In HoMM, both data points (e.g., inputs, outputs) and entire tasks are embedded into a shared latent space $Z$ . This “homoiconicity” allows tasks to be treated as transformable objects within the same representational domain as raw data, mirroring principles from functional programming.

The architecture consists of domain-specific encoders, a meta network $\mathcal{M}$ that summarizes sets of (input, target) pairs into a function embedding $z^{\text{func}}$ , and a hyper-network $\mathcal{H}$ that produces parameters for a downstream transformation network $F$ . Critically, meta-mapping enables the transformation of cached task embeddings; for instance, to adapt a task for “playing to win” into “playing to lose,” the network applies learned meta-mapping transformations in $Z$ . This machinery is used for both basic task prediction and for meta-level task modifications:

$\hat{y} = \mathcal{O}(F_{z^{\text{func}}}(\mathcal{I}(x)))$

with $z^{\text{func}} = \mathcal{M}(\{(\mathcal{I}(x_0), \mathcal{T}(y_0)), ...\})$ and $F_{z^{\text{func}}}$ parameterized by $\mathcal{H}$ acting on $z^{\text{func}}$ .

2. Meta-Prompting: Structural Abstraction and Prompt Engineering

Meta-prompting, as formalized in (Zhang et al., 2023), shifts prompt design from content-specific examples to structural guides abstracted over task domains. By modeling the mapping between tasks ( $\mathcal{T}$ ) and structured prompts ( $\mathcal{P}$ ) as a functor $\mathcal{M}: \mathcal{T} \to \mathcal{P}$ , meta-prompting guarantees that compositional properties of task transformations are preserved in the space of prompt templates:

$\mathcal{M}(g \circ f) = \mathcal{M}(g) \circ \mathcal{M}(f)$

Recursive meta-prompting (RMP), modeled as a monad over the prompt category, enables the Qwen-72B model to generate, refine, and self-improve its prompts autonomously. This recursive refinement loop is governed by unit and multiplication natural transformations $(\eta, \mu)$ that ensure associativity and stability across nested prompt modifications.

3. Zero-Shot Adaptation via Meta-Learning

Meta-learning frameworks (Verma et al., 2019, Li et al., 2021) are adapted to the zero-shot domain by partitioning tasks into disjoint “seen” and “unseen” classes and explicitly training models to synthesize class features and representations conditioned on semantic attributes. The episodic meta-learning approach updates model parameters to facilitate rapid adaptation even with few or no gradient steps for unseen classes. Generative modeling, including Wasserstein GANs (Verma et al., 2019) and attribute-modulated networks (Li et al., 2021), facilitates robust sample synthesis and eliminates bias toward seen classes.

In a LLM context, such as Qwen-72B, meta-learning principles can be employed to optimize adaptation to novel task descriptors, prompt rephrasings, or attribute-rich instructions, often lowering the sample complexity and enabling reliable task modifications through prompt manipulation.

4. Prompt Optimization and Consistency for Generalization

Effective zero-shot performance is highly sensitive to prompt design (Orlanski, 2022, Mu et al., 2023, Zhou et al., 2022). Empirical studies demonstrate that including explicit answer choices, optimizing prompt length (ideally 14–21 tokens), and using “unseen” prompt templates (not present during pre-training) can lead to substantial gains in accuracy and robustness.

The prompt consistency methodology (Zhou et al., 2022) regularizes model outputs across semantically equivalent prompts by minimizing the “swarm distillation” loss:

$\mathrm{Loss} = -\mathbb{E}_x \mathbb{E}_{r^{(i)}, r^{(j)}}\left[\mathbb{E}_{\hat{y} \sim \hat{q}(\cdot|x, r^{(i)})} \log p_\theta(r_y^{(j)}(x))\right]$

This encourages the Qwen-72B model to provide stable predictions regardless of prompt phrasing and can be performed both with unlabeled data and directly at inference time.

5. Meta-Prompting for Automated Multimodal Recognition

Meta-prompting techniques have been extended to visual recognition domains (Mirza et al., 18 Mar 2024). The Meta-Prompting for Visual Recognition (MPVR) strategy automates the generation of category-specific prompts for Vision-LLMs (VLMs) by leveraging minimal input (task description and label list). MPVR uses a two-stage process: first, it prompts the LLM for diverse query templates; second, it generates detailed, category-specific prompts by substituting explicit labels. These prompts are ensembled to form robust zero-shot classifiers, significantly outperforming manual prompt baselines in accuracy across 20 benchmarks.

This mechanism suggests that Qwen-72B, equipped with meta-prompting, can autonomously synthesize optimized multimodal prompts, thereby extending zero-shot capabilities to image classification, retrieval, and generalized cross-modal reasoning.

6. Empirical Performance and Token Efficiency

Meta-prompted architectures yield substantial numerical gains on complex tasks. The zero-shot meta-prompted Qwen-72B model achieves a PASS@1 accuracy of 46.3% on the MATH benchmark (compared to 42.5% for the first release of GPT-4) and 83.5% on GSM8K, outperforming traditional few-shot and fine-tuned approaches (Zhang et al., 2023). Token efficiency is a notable advantage; abstract, example-agnostic meta-prompts require fewer tokens than lengthy few-shot templates, allowing fairer model comparisons and more efficient resource utilization.

Performance improvements in zero-shot visual recognition (up to 19.8% over CLIP baselines) are attributed to automated prompt ensembling (Mirza et al., 18 Mar 2024). Improvements in reading comprehension and numerical reasoning (EchoPrompt: +5–13%, (Mekala et al., 2023)) further validate the general applicability of meta-prompting strategies.

7. Modular Reasoning, Self-Improvement, and Multimodal Extensions

Meta-prompting reinforces the model’s ability to perform modular, compositional reasoning by decomposing complex tasks into structured procedural guides. Recursive frameworks enable self-improvement, allowing the model to autonomously refine its prompting strategy via monadic compositionality. This structure naturally generalizes to multi-modal settings—such as combining textual, visual, and code-processing capabilities—by leveraging type-theoretic formalism and XML-like schema for concurrent prompt decomposition (Zhang et al., 2023).

The integration of meta-prompting, meta-learning, and prompt consistency in Qwen-72B facilitates unified handling of instructions, data, and meta-instructions. This architecturally parsimonious approach positions the model for robust adaptation, efficient resource use, and state-of-the-art zero-shot generalization across linguistic and multimodal domains.

In summary, a Zero-Shot Meta-Prompted Qwen-72B Model synthesizes structural meta-prompting, meta-learning, and prompt optimization within large-scale transformer frameworks. This yields flexible, modular, and efficient zero-shot capabilities distinguished by shared latent task representations, automated prompt engineering, and empirical performance aligned with the most challenging tasks in natural language processing and beyond.