MLLM-Assisted Conformity Enhancement

• MLLM-Assisted Conformity Enhancement is a paradigm that uses multimodal large language models to enforce and refine content conformity to predefined structural and semantic constraints.
• It employs techniques like visual-textual alignment, schema validation, and instruction tuning with preference optimization to enhance output consistency and data efficiency.
• Applications include e-commerce data curation, federated learning, ensemble approaches, and generative pipelines, driving robust performance in multimodal and heterogeneous environments.

MLLM-Assisted Conformity Enhancement (MACE) designates a set of methodologies and system architectures that leverage multimodal LLMs (MLLMs) to enforce, refine, or measure conformity between machine-generated content and predefined structural, semantic, or alignment constraints. The paradigm is particularly influential in domains where structured schema adherence, multimodal grounding, or resilience to data/domain heterogeneity is required. Approaches under this umbrella span structured data curation in e-commerce, multi-conformer and multi-modal ensemble learning, privacy-aware federated learning, and robust symbolic/gradient-based generative pipelines. Advances in MACE have demonstrated superior data efficiency, output consistency, and adaptability across numerous application verticals.

1. Principles and Mechanisms of MACE

MLLM-Assisted Conformity Enhancement exploits the cross-modality semantic understanding afforded by large pre-trained models to identify, enforce, and optimize content conformity:

• Visual-Textual Conformity: MLLMs, often with vision-language capabilities, act as filters or editors by removing non-grounded elements from textual or structured data (e.g., titles, key–value aspect pairs in listings), retaining only those aligned with verifiable visual features. In practice, this is achieved by prompting a large MLLM (e.g., InternVL2.5-78B) to rewrite fields, discarding tokens or attributes not evidenced in the visual input (Zhang et al., 13 Aug 2025).
• Schema Alignment: Conformity enhancement ensures that machine-generated or curated content adheres to schema or domain-specific constraints by preserving only visually supported or contextually coherent tokens, facilitating robust fine-tuning and reducing hallucination rates.
• Instruction and Preference Supervision: Downstream models are trained using outputs produced by MLLMs via negative log-likelihood minimization, often in conjunction with direct preference optimization (DPO). The DPO loss steers the model to prefer outputs confirmed as superior by a ranking or judge model, further reinforced by a KL regularization term to ensure alignment with a reference policy.

These core methods form the foundation for MLLM-assisted data refinement and compliance in structured generation contexts.

2. Role in Multimodal E-commerce Systems

MACE is exemplified by the OPAL framework for e-commerce (Zhang et al., 13 Aug 2025), addressing the modality gap between item images and schema-constrained textual descriptions:

• Preprocessing and Data Filtering: Noisy product listings are processed using MACE to produce conformity-enhanced entries. The process explicitly removes unverifiable or spurious key–value pairs and promotional/noise tokens from item titles by leveraging the grounded visual understanding of a state-of-the-art MLLM.
• Instruction Tuning and Preference Optimization: After MACE, the filtered dataset is further expanded via LLM-Assisted Contextual Understanding (LACU), introducing fine-grained, contextually nuanced dialogue. The complete data pipeline is then used to fine-tune the downstream MLLM with visual instruction tuning (minimizing

$$\mathcal{L}_{\text{visual}} = - \sum_{t=1}^T \log P_\theta(y_t \mid y_{<t}, x)$$

) and DPO loss:

$$\mathcal{L}_{\mathrm{pref}} = -\log \sigma \left( \beta \left[\log \pi_{\theta} (y_{\text{chosen}}\,|\,x) - \log \pi_{\theta}(y_{\text{rejected}}\,|\,x)\right]\right) + \lambda \cdot \text{KL}(\pi_{\theta} \| \pi_{\text{ref}})$$

ensuring schema consistency and contextual fidelity.

• Outcome: The framework achieves a minimum 50% improvement in ROUGE-L F1 and a 16% gain in aspect-matching F1 on real-world e-commerce datasets, with superior schema recall over retrieval or non-conformity–enhanced generation (Zhang et al., 13 Aug 2025).

3. Relation to Molecular Conformer and Ensemble Learning

While initial developments of conformity enhancement emerged in domains such as cheminformatics, the analogous principles apply:

• Manifold Conformity: When learning from molecular structures, MLLM (or model)-assisted approaches promote the creation of latent representations where conformers of the same molecule cluster together in embedding space (i.e., manifold smoothness is enhanced as in SupSiam, where non-contrastive auxiliary loss is used).
• Ensemble and Multi-Instance Aggregation: Diverse conformers are processed and their embeddings aggregated using set encoders (e.g., DeepSets, attention pooling) to yield representations resilient to geometric or conformer noise (Zhu et al., 2023). These methods may be fused with MLLM-based feature weighting or re-ranking to further enhance cross-task conformity.

This suggests a direct analogy between visual-textual conformity enforced by MLLMs and chemical/structural conformity established by geometric or ensemble methods.

4. Integration in Instruction Tuning and Federated Learning

MLLM-assisted conformity is also employed in model training for heterogeneous and distributed environments:

• Instruction Tuning with Domain Conflicts: Sparse mixture-of-experts designs (as in LLaVA-MoLE) leverage token-level routing to assign model submodules to disjoint or conflicting instruction domains. The resultant model exhibits enhanced conformity—manifested as uniform cross-domain task performance—by mitigating cross-domain training conflicts at the architectural level (Chen et al., 29 Jan 2024).
• Privacy-Preserving Federated Learning: In MLLM-LLaVA-FL, MLLMs annotate and align large, heterogeneous image–text data at the server. The compact federated model $g_\text{fl}$ is distilled using dynamic weighted feature blending as

$$Z_v = (1-\alpha) \cdot g(X_v) + \alpha \cdot g_\text{fl}(X_v)$$

and, upon global aggregation, further aligned under MLLM supervision using a cross-entropy plus KL loss. This results in improved accuracy and class fairness, especially in long-tailed, client-specific settings (Zhang et al., 9 Sep 2024).

5. Evaluating and Enhancing Conformity in LLMs

Conformity is also a behavioral property of LLMs:

• Susceptibility to Majority Influence: LLMs demonstrate conformity effects analogous to human psychological behaviors; their answers can shift toward a (possibly incorrect) majority when primed by other model responses (Zhu et al., 16 Oct 2024). The conformity level is quantified as

$$\mathrm{CL}_p(S, p; \mathrm{LMe}) = \frac{1}{|S|} \sum_{i=1}^{|S|} \mathbb{I}(\hat{a}_i = c)$$

where $\mathbb{I}$ denotes the indicator function, $\hat{a}_i$ is the model's answer after group exposure, and $c$ is the consensus answer.

• Mitigation Strategies: Prompt-level interventions such as Devil’s Advocate (injection of dissent) and Question Distillation (summary reformulation) can reduce conformity, with empirical reductions in CL for susceptible models without retraining.
• Distributional Alignment: To align LLM-generated judgment distributions with real human judgment uncertainty (moving beyond single-point/hard label alignment), recent frameworks propose a KL-based objective:

$$\mathcal{L}_{\mathrm{KL}}(\theta) = \frac{1}{|D|} \sum_x D_{\mathrm{KL}}(p(x) \parallel q_\theta(x))$$

optionally combined with adversarial training over the space of plausible empirical distributions, further increasing reliability and fidelity (Chen et al., 18 May 2025).

6. Extensions to Data-Centric and Generative Feature Engineering

MLLM-assisted conformity enhancement extends to generative and data-centric regimes:

• Data Curation via Adaptive Enhancement: Adaptive Image-Text Quality Enhancer (AITQE) leverages MLLMs to dynamically score and selectively rewrite image–caption pairs only where semantic alignment is inadequate, optimizing data utility while preserving maximal data volume (Huang et al., 21 Oct 2024).
• Hybrid Symbolic–Gradient Generative Pipelines: MLLMs can be teamed with ML-based embedding search in generative feature transformation, ensuring both symbolic validity (from LLMs) and stability/robustness (from gradient search in latent spaces). Final outputs are generated via a product-of-experts combination:

$$P(w_t) = [P_{\text{ML}}(w_t | z_{\text{new}}, w_{<t})]^{\lambda} [P_{\text{LLM}}(w_t | \Gamma, w_{<t})]^{1-\lambda} / Z$$

yielding high-performing, syntactically valid feature transformations (Wang et al., 10 Jun 2025).

7. Broader Implications and Applications

MLLM-assisted conformity enhancement is broadly applicable wherever multimodal data, structural schema adherence, domain-specific robustness, or human-like output alignment are required. Demonstrated applications include:

• Automated product listing optimization in large-scale e-commerce systems, with enhanced accuracy and schema consistency.
• Robust model training and evaluation in distributed, privacy-sensitive, or heterogeneous environments.
• Improved dataset curation and filtering for multimodal pretraining.
• Adaptive multi-agent or collaborative LLM systems, where prompt-level conformity mitigation can prevent pathological model agreement or error propagation.

A plausible implication is that as the complexity of real-world data and schema constraints grow, MACE-style architectures and preprocessing pipelines will become standard practice in both research and industry deployment for multimodal and human-in-the-loop AI systems.