Challenge: Recent advances in foundation models have impeded the ability for these fields to benefit from each other’s advancements.
Approach: They propose to use a multimodal foundation model to unify various modalities and recommendation tasks under the P5 recommendation paradigm to implement personalized prompts.
Outcome: The proposed model will unify visual, textual, and personalization modalities under the P5 recommendation paradigm and will improve recommendation performance and efficiency.

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Challenge: Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)

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Challenge: Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM.
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M5 – A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks (2024.findings-emnlp)

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Challenge: Recent advances in Large Language Models and their multimodal counterparts have shown significant performance disparities across different languages and cultural contexts.
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CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023.emnlp-main)

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Challenge: Existing code LLMs adopt a specific architecture or rely on a unified encoder-decoder network for downstream tasks, lacking flexibility to operate in the optimal architecture for a particular task.
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LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models (2024.naacl-demo)

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Challenge: Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches.
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Multimodal Large Language Models for Human-AI Interaction: Foundations, Agents, and Inclusive Applications (2026.eacl-tutorials)

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Challenge: This tutorial presents foundations, agentic capabilities, and inclusive applications of multimodal large language models.
Approach: This tutorial presents foundations, agentic capabilities, and inclusive applications of multimodal large language models.
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Concept-pedia: a Wide-coverage Semantically-annotated Multimodal Dataset (2025.emnlp-main)

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Challenge: Current evaluations for Vision-language Models remain heavily anchored to ImageNet .
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Vision-Language Pretraining: Current Trends and the Future (2022.acl-tutorials)

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Challenge: Recent vision-language models are being used for downstream tasks that require large datasets and supervised datasets.
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks (2026.findings-acl)

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Challenge: Survey aims to identify challenges of multimodal unlearning for vision, language, audio and video . retraining after deletion requests or policy updates is often impractical, survey finds .
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EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs (2024.acl-long)

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Challenge: Existing multimodal models that depend on encoders like CLIP or ImageBind need ample amounts of training data to bridge modalities.
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