MM-ChatAlign: A Novel Multimodal Reasoning Framework based on Large Language Models for Entity Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing MMEA methods rely on knowledge representation learning (KRL) to measure the similarity of entity embeddings. |
| Approach: | They propose a framework that utilizes the visual reasoning abilities of MLLMs for multimodal entity alignment. |
| Outcome: | The proposed framework integrates the visual reasoning abilities of MLLMs for multimodal entity alignment. |
Similar Papers
SGMEA: Structure-Guided Multimodal Entity Alignment (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods focus on interactions between neighboring entities in the structural modality while neglecting interactions between entities in visual and attribute modalities. |
| Approach: | They propose a structure-guided multimodal entity alignment method which prioritizes structural information from knowledge graphs to enhance the visual and attribute modalities. |
| Outcome: | The proposed method achieves state-of-the-art performance across multiple datasets, validating its effectiveness and superiority in practical applications. |
Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing Multimodal Large Language Models (MLLMs) are predominantly trained on consistent visual-textual inputs, leaving open the question of whether they can handle semantic mismatches in layout-rich content. |
| Approach: | They propose to use multimodal inconsistency reasoning to assess MLLMs' ability to reason about semantic mismatches in webpages, presentation slides, and posters. |
| Outcome: | The proposed model outperforms open-source models in detecting inconsistencies in webpages, presentation slides, and posters while remaining vulnerable to inconsistent errors. |
Multi-Modal Entities Matter: Benchmarking Multi-Modal Entity Alignment (2025.coling-main)
Copied to clipboard
| Challenge: | Existing MMEA datasets consider multi-modal data as attributes of textual entities, neglecting correlations between the multi-modal data. |
| Approach: | They propose a multi-modal entity alignment dataset that models multi-dimensional data as textual entities in the MMKG. |
| Outcome: | The proposed dataset can learn the structural information of entities by considering both intra-modal and cross-modal relations and infer the similarity of different types of entity pairs. |
MM-LLMs: Recent Advances in MultiModal Large Language Models (2024.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed models preserve the reasoning and decision-making capabilities of LLMs and empower diverse range of MM tasks. |
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing multimodal large language models are trained on single-turn vision question-answering tasks, which do not accurately reflect real-world human conversations. |
| Approach: | They propose a large-scale multi-turn multimodal dialogue dataset that uses rules and GPT assistance to generate a multi-turned multimodal dialog dataset. |
| Outcome: | The proposed dataset is a strong benchmark for multi-turn multimodal dialogue learning . it features complex dialogues with contextual dependencies that force models to track, ground, and recall information across multiple turns and disparate visual regions. |
Exploring and Evaluating Multimodal Knowledge Reasoning Consistency of Multimodal Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | MLLMs have achieved significant breakthroughs in understanding across text and vision, but current models still face inconsistencies in reasoning outcomes. |
| Approach: | They propose to evaluate multimodal large language models using a multimodal knowledge reasoning dataset to examine the extent of consistency degradation. |
| Outcome: | The proposed evaluation tasks show that MLLMs are inefficient at integrating knowledge across modalities . |
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)
Copied to clipboard
Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang
| Challenge: | a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling . |
| Approach: | They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution. |
| Outcome: | The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data. |
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs (2025.emnlp-main)
Copied to clipboard
Yuanyang Yin, Yaqi Zhao, Yajie Zhang, Yuanxing Zhang, Ke Lin, Jiahao Wang, Xin Tao, Pengfei Wan, Wentao Zhang, Feng Zhao
| Challenge: | Multimodal Large Language Models (MLLMs) integrate visual and textual inputs, yet modality alignment remains one of the most challenging aspects. |
| Approach: | They propose a token-level supervision alignment method that enables more precise visual-text alignment during pretraining. |
| Outcome: | The proposed method improves performance across various model sizes, with smaller models benefiting the most. |
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-modal entity alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). |
| Approach: | They propose a novel MMEA transformer that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance alignment task. |
| Outcome: | The proposed transformer hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task. |
Unlocking the Power of Large Language Models for Entity Alignment (2024.acl-long)
Copied to clipboard
Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang
| Challenge: | Entity Alignment (EA) is a crucial step in unifying data from heterogeneous sources and plays a critical role in data-driven AI applications. |
| Approach: | They propose a framework that incorporates large language models to improve EA. |
| Outcome: | The proposed framework incorporates large language models (LLMs) to improve EA accuracy while preserving efficiency. |