MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora (2025.emnlp-main)
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| Challenge: | Existing approaches to update model-based indexes with new documents are expensive and require expensive retraining. |
| Approach: | They propose a framework that combines an expandable mixture of Low-Rank Adaptation experts with a layer-wise out-of-distribution-driven expansion strategy. |
| Outcome: | Experiments on NQ320k and MS MARCO Passage show that the proposed framework outperforms full-model update baselines with minimal parameter overhead and substantially lower training costs. |
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| Challenge: | Existing methods for multitask learning fail to match input semantics with expert capabilities, leading to weak expert specialization. |
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| Challenge: | Existing low-rank adaptations have limited expressiveness, a tendency to overfit, and sensitivity to hyperparameter settings. |
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| Challenge: | Recent studies combine LoRA with Mixture-of-Experts (MoE) to improve performance in Large Language Models. |
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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang
| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation. |
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| Challenge: | Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules. |
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| Challenge: | Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance. |
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PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation (2024.findings-eacl)
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| Challenge: | Pre-training/fine-tuning of pre-training models has become more expensive and resource-hungry. |
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TLoRA: Task-aware Low Rank Adaptation of Large Language Models (2026.acl-long)
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| Challenge: | Existing low-rank Adaptation (LoRA) methods address only one factor, often at the cost of increased training complexity or reduced practical efficiency. |
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