Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLMs (2025.findings-emnlp)
Copied to clipboard
Chenkun Tan, Pengyu Wang, Shaojun Zhou, Botian Jiang, Zhaowei Li, Dong Zhang, Xinghao Wang, Yaqian Zhou, Xipeng Qiu
| Challenge: | Recent advances in multimodal large language models focus on improving performance . however, language prior conflict leads to suboptimal vision-language alignment . |
| Approach: | They propose a method to decouple the alignment process from language prior interference . they use a proxy LLM to detach from language interference during pretraining . |
| Outcome: | The proposed method improves training performance and generalizes training data. |
Similar Papers
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. |
VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)
Copied to clipboard
Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei
| Challenge: | Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision. |
| Approach: | They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone. |
| Outcome: | The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data. |
A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)
Copied to clipboard
Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms. |
| Approach: | They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration . |
| Outcome: | The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining . |
MLLM-Protector: Ensuring MLLM’s Safety without Hurting Performance (2024.emnlp-main)
Copied to clipboard
Renjie Pi, Tianyang Han, Jianshu Zhang, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang
| Challenge: | MLLMs are deployed on limited image-text pairs, which makes them more vulnerable to catastrophic forgetting of their original abilities during safety fine-tuning. |
| Approach: | They propose a plug-and-play strategy that detects harmful visual inputs and transforms harmful ones into harmless ones. |
| Outcome: | The proposed approach mitigates the risks posed by malicious visual inputs without compromising the original performance of MLLMs. |
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models (2026.eacl-long)
Copied to clipboard
Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun, Yingfei Sun
| Challenge: | Existing research treats MLLMs as unified systems optimized through end-to-end training, but the impact of vision encoder’s prior knowledge is seldom investigated. |
| Approach: | They propose a metric to quantify the effect of prior knowledge on MLLM performance by integrating prior knowledge at the vision encoder level into a training framework. |
| Outcome: | The proposed training framework incorporates prior knowledge at the vision encoder level, and significantly boosts visual understanding capabilities of MLLMs. |
PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026.findings-acl)
Copied to clipboard
Zijing Wang, YongKang Liu, Mingyang Wang, Ercong Nie, Deyuan Chen, Zhengjie Zhao, Shi Feng, Daling Wang, Xiaocui Yang, Yifei Zhang, Hinrich Schuetze
| Challenge: | Multimodal instruction fine-tuning degrades textual reasoning capability, undermining multimodal performance. |
| Approach: | They propose a plateau-guided model merging method that selectively injects base language model parameters into MLLMs to mitigate this degradation. |
| Outcome: | The proposed framework reduces multimodal instruction fine-tuning degradation by incorporating a plateau-guided model merging method into MLLMs. |
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Multi-modal large language models (MLLMs) are expected to support multi-turn queries of interchanging image and text modalities in production. |
| Approach: | They propose to use visual-question-answering (VQA) datasets to annotate a 5k-sample VQA preference dataset and to investigate the degradation of VQA datasets. |
| Outcome: | The proposed model surpasses the instruction-following capabilities of the language model with DPO and SteerLM. |
DeepInsert: Early Layer Bypass for Efficient and Performant Multimodal Understanding (2026.eacl-long)
Copied to clipboard
Moulik Choraria, Xinbo Wu, Akhil Bhimaraju, Nitesh Sekhar, Yue Wu, Xu Zhang, Prateek Singhal, Lav R. Varshney
| Challenge: | Recent work shows that hyperscaling of data and parameter count in LLMs is yielding diminishing improvement when weighed against training costs. |
| Approach: | They propose to insert multimodal tokens directly into the middle of the model to bypass the early layers. |
| Outcome: | The proposed method reduces training and inference costs while preserving performance. |
Take a Closer Look at Multilinguality! Improve Multilingual Pre-Training Using Monolingual Corpora Only (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have demonstrated remarkable cross-lingual capability of pre-trained language models . however, semantic alignments may be the reason behind such capability but remain under-explored. |
| Approach: | They propose token-level and semantic-level code-switched masked language modeling to improve cross-lingual interactions over mono-mPLMs without parallel sentences. |
| Outcome: | The proposed method outperforms mono-mPLMs on natural language understanding and unsupervised machine translation tasks. |
Aligners: Decoupling LLMs and Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. |
| Approach: | They propose to decouple LLMs and alignment by training *aligner* models that can be used to align any LLM on an as-needed basis. |
| Outcome: | The proposed model can be used to align any LLM for a given criteria on an as-needed basis. |