Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?

2025-06-21

AI Summary

This work investigates whether synthetically generated images can serve as an effective auxiliary modality for text-centric tasks, thereby bridging the modality gap between unimodal language models and multimodal models. We propose the first systematic evaluation framework that integrates state-of-the-art text-to-image (T2I) models, prompt engineering techniques, and multimodal fusion architectures to enable collaborative learning between language models and synthetic images in text classification. Our key contributions are threefold: (1) the first empirical validation of synthetic images as a viable perceptual modality; (2) the establishment of a dedicated benchmark suite for this paradigm; and (3) the identification of semantic alignment, visual grounding capability, and generation fidelity as critical determinants of performance. Experiments demonstrate that synthetic images consistently enhance strong language model baselines—particularly on tasks with high visual grounding potential—confirming their effectiveness and practical promise.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

A significant ``modality gap" exists between the abundance of text-only data and the increasing power of multimodal models. This work systematically investigates whether images generated on-the-fly by Text-to-Image (T2I) models can serve as a valuable complementary modality for text-centric tasks. Through a comprehensive evaluation framework on text classification, we analyze the impact of critical variables, including T2I model quality, prompt engineering strategies, and multimodal fusion architectures. Our findings demonstrate that this``synthetic perception" can yield significant performance gains, even when augmenting strong large language model baselines. However, we find the effectiveness of this approach is highly conditional, depending critically on the semantic alignment between text and the generated image, the inherent ``visual groundability" of the task, and the generative fidelity of the T2I model. Our work establishes the first rigorous benchmark for this paradigm, providing a clear analysis of its potential and current limitations, and demonstrating its viability as a pathway to enrich language understanding in traditionally unimodal scenarios.

Problem

• Bridging modality gap between text and multimodal models
• Evaluating synthetic images for text-centric tasks
• Assessing conditions for effective text-to-image alignment

Innovation

• Using T2I models for text-centric tasks
• Analyzing T2I quality and prompt strategies
• Synthetic perception enhances language understanding