Multimodal-to-Textual Construction

• Multimodal-to-textual construction is a framework that transforms diverse inputs like images, audio, and tables into a unified textual representation.
• It employs expert model transformations and fusion strategies to mitigate modality gaps, handle noise, and ensure semantic consistency.
• This approach underpins applications in retrieval, interpretable AI, and creative content generation while enhancing scalability and decision explainability.

Multimodal-to-textual construction is a class of methodologies that convert heterogeneous multimodal inputs—such as images, acoustic signals, sensor data, or structured tables—into a unified textual representation. This operational framework enables downstream models, particularly LLMs, to interpret, manipulate, and reason over diverse data using the linguistic abstraction layer provided by natural language. In current research, multimodal-to-textual construction facilitates efficient knowledge alignment, robust downstream inference, scalable retrieval, explainability, and new paradigms for creative content generation, all while addressing issues such as modality gap, robustness to noise or missing modalities, and information loss due to modality transformation.

1. Foundations of Multimodal-to-Textual Construction

The essential principle in multimodal-to-textual construction is the transformation of non-linguistic data streams into text via learned or heuristic mappings, using either expert models (e.g., image captioning networks, speech-to-text models, table serialization frameworks) or unsupervised procedures. Each modality is handled by a dedicated function $f_i$ such that for a modality $M_i$ (e.g., image, audio, tabular, sensor), its textual description is $T_i = f_i(M_i)$. This strategy reduces task and data heterogeneity, enabling uniform preprocessing and analysis via a single model class—usually an LLM. The approach is widely adopted for behavioral analysis (Hasan et al., 2023), retrieval and classification (Hanu et al., 2023), robust multimodal alignment (Yen et al., 6 Jul 2024), knowledge graph construction (Liu et al., 17 Mar 2025), explainable decision modeling (Zarghani et al., 10 Jul 2025), dataset curation (León et al., 6 Sep 2025), and creative text generation (Chen et al., 22 Aug 2025).

The resulting pipeline translates hard-to-align or non-standard modalities (e.g., dense vectors, audio, high-dimensional images) into a text-centric “modality space” where linguistic relations and reasoning are naturally handled. This parametrization is especially attractive in low-resource settings or when robust and explainable systems are required.

2. Methodological Variants and Architectures

Multiple frameworks exist for multimodal-to-textual construction, with technical distinctions arising from the choice of expert models, fusion strategies, and the role of textualization in the overall system architecture.

Expert Model Transformation:

• Vision-to-text: Image captioning transformers (e.g., BLIP, BLIP-2) produce detailed textual scene or object descriptions (León et al., 6 Sep 2025), often structured for succinctness and informativeness.
• Audio-to-text: Speech-to-text models like Whisper convert speech to transcriptions, while interpretable acoustic feature extraction (pitch, jitter, shimmer) is mapped to categorical descriptions via clustering (e.g., “high shimmer, normal pitch”) (Hasan et al., 2023).
• Tabular-to-text: Structured table serialization is performed, as in TabLLM, where columns are represented with templated sentences (“The column name is values”) (Yen et al., 6 Jul 2024).

Fusion and Summarization:

The outputs from modality-specific transformations are either concatenated or fed into an LLM prompt, sometimes with explicit instructions for unification or summarization. Advanced methods apply LLM-driven summarization functions $S(\{T_i\})$ to compress and harmonize the multi-source texts, followed by chain-of-thought (CoT) reasoning $g(T)$ for enhanced inference or prediction (Yen et al., 6 Jul 2024). Attention-based cross-modal fusion layers have been proposed to augment input features, for example by aligning the semantics of corresponding visual and textual features via gated or attention-driven mechanisms (Chen et al., 22 Aug 2025).

Iterative and Retrieval-Augmented Construction:

Retrieval-augmented methods (RAG) map images into a language embedding space via linear projections and retrieve nearest textual anchors to feed as augmented context to the LLM (Jaiswal et al., 6 Aug 2025). Further, iterative optimization introduces synthetic captions, continuously refining the mapping using feedback from caption quality metrics (e.g., BLEU, SPICE, CIDEr-D).

Robustness and Semantic Filtering:

Recent frameworks combine expert model outputs with explicit robustness mechanisms such as noise filtering, semantic alignment gating, and LLM-based redundancy minimization. The text construction is reinforced with summaries that restore or infer missing content in the presence of modality dropout or degradation (Yen et al., 6 Jul 2024). Semantic gating with probabilistic masking ensures that redundant modalities do not overwhelm the reasoning process, especially in creative or composite tasks (Chen et al., 22 Aug 2025).

3. Challenges: Modality Gap, Robustness, and Semantic Consistency

Modality Gap:

The inherent misalignment between visual and textual embeddings ("modality gap") persists despite large-scale pre-training of multimodal models. Naïvely combining or fine-tuning such models often fails to adequately bridge this gap but simple lightweight mappings—such as OLS-based linear projections—have been shown to mitigate the issue efficiently, especially when combined with RAG and iterative refinement (Jaiswal et al., 6 Aug 2025).

Robustness to Missing/Noisy Modalities:

Standard text-centric methods may be vulnerable to missing data, noise injection, or incomplete observations. Studies reveal that multimodal-to-textual summarization, via LLM-constructed summaries and reasoning, results in higher downstream robustness—measured as lower accuracy drop ratios under noise—relative to alternative fusion methods (e.g., 4.5% vs. 11.2% drop under severe perturbations) (Yen et al., 6 Jul 2024). Further, chain-of-thought reasoning allows models to fill gaps by leveraging knowledge from non-corrupted modalities.

Semantic Inconsistency and Redundancy:

Alignment mechanisms such as semantic similarity gating and attention-based cross-modality fusion explicitly control for incoherent or duplicative content, ensuring that only the most complementary aspects of each modality are included in the final textual representation (Chen et al., 22 Aug 2025). These mechanisms are essential in creative tasks, where conflicting semantics across generated images and text can compromise narrative coherence and creativity.

4. Evaluation Protocols, Datasets, and Empirical Results

Empirical Evaluation:

Multimodal-to-textual construction methods have been benchmarked on a wide range of tasks:

• Accuracy and F1 score on multimodal fact-checking (e.g., achieving 0.84 weighted F1 on Factify 2 (Kishore et al., 7 Aug 2025));
• BLEU, ROUGE, CIDEr, and SPICE for generated descriptions (Jaiswal et al., 6 Aug 2025);
• Robustness under synthetic perturbations on PetFinder multimodal datasets (Yen et al., 6 Jul 2024);
• Style consistency, BertScore, and cross-modal agreement on creative writing tasks (Chen et al., 22 Aug 2025).

Dataset Construction:

Careful curation strategies favor high-quality, temporally and semantically coherent segments, for example aligning one-second audio with mid-frame images and using beam-searched captions with minimum and maximum token constraints (León et al., 6 Sep 2025). These strategies yield large-scale multimodal corpora (>2 million audio-image-text triplets), supporting both transfer learning and cross-modal retrieval.

5. Applications and Significance

Information Retrieval and Search:

By encoding visual, auditory, and structured data as natural language, systems can leverage advanced LLMs for semantic search, interactive refinement, and zero-shot classification (Sadeh et al., 2019, Hanu et al., 2023). Retrieval-augmented description generation facilitates precise content-based and cross-modal queries (Jaiswal et al., 6 Aug 2025).

Robust and Interpretable AI:

Standardizing all inputs to text enhances interpretability and model transparency; textualized non-verbal cues make model decisions more accessible and auditable (Hasan et al., 2023). LLM-driven explanations (e.g., in autonomous driving) bridge the black-box gap between sensor fusion and actionable narratives (Zarghani et al., 10 Jul 2025).

Scalable Dataset Creation:

Pipelines that extract synchronized, semantically unified multimodal data from raw videos provide large-scale resources for downstream multimodal learning tasks, including event detection, image-to-text, and audio-conditioned generation (León et al., 6 Sep 2025).

Creative Writing and Content Generation:

Multimodal input, when synthesized into textual stories, supports illustration-rich article generation, marketing content, and innovative creative applications, particularly when semantic alignment and creativity optimization modules are incorporated (Chen et al., 22 Aug 2025).

6. Limitations and Future Directions

Despite their efficiency and generality, text-centric multimodal methods face several open challenges. The reliance on expert models for initial modality textualization means downstream performance can be bottlenecked by errors or biases in those models (Hasan et al., 2023). Richness and nuance of the original modalities may be diluted in the textual transformation process. Advances in context-aware or probabilistic textualization, adaptive gating of semantic content, and end-to-end learning of both extraction and textualization components could address these shortcomings.

Research aims include developing dynamic systems that adapt to missing or noisy modalities more gracefully, incorporating feedback mechanisms to optimize textualization quality, and integrating more sophisticated retrieval-generation hybrids for knowledge-intensive tasks (Jaiswal et al., 6 Aug 2025, Yen et al., 6 Jul 2024). There is increasing interest in benchmarking across domains with real-world constraints on modality quality and availability, as well as in creating richer datasets for evaluating the semantic and factual integrity of multimodal-to-textual pipelines.

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In summary, multimodal-to-textual construction constitutes a critical paradigm for aligning, reasoning over, and scaling heterogeneous data in modern AI systems, drawing on a combination of expert model transformations, robust summarization and fusion schemes, lightweight alignment mechanisms, and interpretability enhancements. Its application spans robust AI, knowledge graph augmentation, large-scale dataset creation, explainable autonomous systems, creative content generation, and beyond—anchoring multimodality in the lingua franca of text for comprehensive, flexible, and robust machine reasoning.