Papers with MCIT
Progressive LoRA for Multimodal Continual Instruction Tuning (2025.findings-acl)
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| Challenge: | Existing approaches to MCIT address Catastrophic Forgetting and Knowledge Transfer (KT) but using a fixed number of shared LoRA blocks across tasks can lead to knowledge interference. |
| Approach: | They propose a framework that uses a fixed number of shared LoRA blocks to reduce knowledge interference. |
| Outcome: | The proposed framework outperforms existing approaches on the latest MCIT benchmark. |
Enhancing Multimodal Continual Instruction Tuning with BranchLoRA (2025.acl-long)
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| Challenge: | Existing approaches to fine tune Multimodal Large Language Models (MLLMs) are prone to Catastrophic Forgetting (CF) existing approaches rely on the Mixture-of-Experts (MoE) LoRA framework to preserve previous instruction alignments. |
| Approach: | They propose an asymmetric tuning-freezing mechanism to mitigate parameter inefficiency . branch-specific routers are introduced to ensure optimal branch distribution over time . |
| Outcome: | The proposed framework outperforms existing frameworks on the latest MCIT benchmarks. |
From Experts to Bases: Orthogonal Subspace Mixture for Continual Multimodal Instruction Tuning (2026.acl-long)
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| Challenge: | Existing parameter-efficient approaches to multimodal Continual Instruction Tuning suffer from knowledge interference and inefficient capacity expansion, limiting scalability. |
| Approach: | They propose a framework for multimodal Continual instruction tuning that decomposes adaptation weights into a globally shared pool of orthonormal bases to capture task-invariant knowledge. |
| Outcome: | Experiments show that MoBLoRA outperforms state-of-the-art methods while maintaining superior parameter efficiency. |
ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt (2025.emnlp-main)
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| Challenge: | Existing MCIT methods do not fully exploit the unique attribute of Large Multimodal Models and often gain performance at the expense of efficiency. |
| Approach: | They propose a multimodal continual instruction learning framework that exploits the ability of LMMs to learn mixed instruction datasets and prompts for each task. |
| Outcome: | The proposed framework achieves +14.26% performance gain on MCIT benchmarks with remarkable x1.42 inference speed free from growing computation. |