Papers with task selector

2 papers
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.
Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation (2023.findings-acl)

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Challenge: Existing few-shot learning-based models have difficulty alleviating the long-tail issue on low-resource KGs because of the lack of training tasks.
Approach: They propose a few-shot low-resource knowledge graph completion framework that generates and selects beneficial few- shot tasks that complement current tasks.
Outcome: The proposed framework is based on several real-world knowledge graphs and validates on multiple domains.

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