Exploring the Capability of Multimodal LLMs with Yonkoma Manga: The YManga Dataset and Its Challenging Tasks (2024.findings-emnlp)
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| Challenge: | YManga dataset is the first specifically designed for yonkoma manga understanding . |
| Approach: | They propose to use a dataset of 1,015 yonkoma strips with 10,150 human annotations to define three tasks for panel sequence detection, intent generation and description generation for masked panels. |
| Outcome: | The proposed dataset contains 1,015 high-quality yonkoma strips with 10,150 human annotations. |
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Context-Informed Machine Translation of Manga using Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Automated manga translation is a promising potential solution, but it is underdeveloped due to the need to incorporate visual elements into the translation process to resolve ambiguities. |
| Approach: | They propose a method that leverages the vision component of multimodal large language models to improve translation quality and evaluate the impact of translation unit size, context length, and propose 'token efficient' approach for manga translation. |
| Outcome: | The proposed method achieves state-of-the-art results for Japanese-English translation and sets a new standard for Japanese and Polish translation. |
Probing Multimodal Large Language Models for Global and Local Semantic Representations (2024.lrec-main)
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| Challenge: | Existing studies have focused on the ability of MLLMs to generate single tokens one by one, while lacking studies about how their representation vectors can encode global multimodal information. |
| Approach: | They propose to use image-caption corpus to train Multimodal Large Language Models (MLLMs) . they find that the topmost layers encode more global semantic information . |
| Outcome: | The proposed models can encode more global semantic information, rather than the topmost layers, and perform better on visual-language entailment tasks. |
MM-LLMs: Recent Advances in MultiModal Large Language Models (2024.findings-acl)
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| Challenge: | MultiModal Large Language Models (MM-LLMs) have undergone significant advances in the past year . traditional MM models incur substantial computational costs, especially when trained from scratch . |
| Approach: | They propose a taxonomy encompassing 126 MM-LLMs and summarize key training recipes to enhance their potency. |
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Unveiling Multimodal Processing: Exploring Activation Patterns in Multimodal LLMs for Interpretability and Efficiency (2025.findings-emnlp)
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| Challenge: | Recent advances in multimodal large language models have remained opaque. |
| Approach: | They propose a method to convert dense MLLMs into fine-grained Mixture-of-Experts architectures. |
| Outcome: | The proposed method outperforms random expert pruning and sparse activation and model pruning. |
Self-Improvement in Multimodal Large Language Models: A Survey (2025.findings-emnlp)
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| Challenge: | Using data and data, self-improvement for Large Language Models has improved model capabilities without significantly increasing costs. |
| Approach: | This survey provides a comprehensive overview of self-improvement for Large Language Models . it includes commonly used evaluations and downstream applications . |
| Outcome: | The authors provide a comprehensive overview of self-improvement in Multimodal LLMs. |
From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning, Efficiency and beyond (2024.lrec-tutorials)
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| Challenge: | This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs. |
| Approach: | This tutorial will review cutting-edge research in MLLMs and examine the impact of ML in learning and reasoning. |
| Outcome: | This course will review cutting-edge research in MLLMs and examine the impact of ML models on learning, learning, and multimodal reasoning. |
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language Model (2024.emnlp-main)
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| Challenge: | Existing MLLMs have a visual question answering capability but lack domain-specific information. |
| Approach: | They propose a framework for language model modules in MLLMs when handling projected image features and verify this hypothesis using logit lens. |
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Towards Unified Multimodal Large Language Models: A survey (2026.findings-acl)
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| Challenge: | unified multimodal large language models (MLLMs) are emerging but lack a systematic framework to connect them and situate current trends within a broader landscape. |
| Approach: | They present a systematic review of unified Multimodal Large Language Models . they outline the foundational concepts and prerequisites for understanding them . |
| Outcome: | The present review provides a systematic and systematic overview of unified MLLMs . it discusses persistent challenges and identify promising directions for future research . |
From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable performance across various tasks, effectively following instructions to meet diverse user needs. |
| Approach: | They propose a framework for evaluation benchmarks and attack techniques for LLMs and MLLMs to enhance their security. |
| Outcome: | The proposed frameworks have been exploited to exploit the weaknesses of LLMs and MLLMs. |
Everything you need to know about Multilingual LLMs: Towards fair, performant and reliable models for languages of the world (2023.acl-tutorials)
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| Challenge: | Responsible AI issues such as fairness, bias and toxicity will be discussed in this tutorial . |
| Approach: | This tutorial will describe various aspects of scaling up language technologies to many of the world’s languages by describing the latest research in Massively Multilingual Language Models (MMLMs). |
| Outcome: | This tutorial will cover various aspects of scaling up language technologies to many of the world's languages by describing the latest research in multilingual models. |