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MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning
Machine Learning · Submitted 10 Dec 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Overall, MoDULA provides a scalable, cost-effective solution for fine-tuning LLMs with enhanced parameter efficiency and generalization capability.
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The abstract, sentence by sentence
The growing demand for larger-scale models in the development of \textbf{L}arge \textbf{L}anguage \textbf{M}odels (LLMs) poses challenges for efficient training within limited computational resources. Traditional fine-tuning methods often exhibit instability in multi-task learning and rely heavily on extensive training resources. Here, we propose MoDULA (\textbf{M}ixture \textbf{o}f \textbf{D}omain-Specific and \textbf{U}niversal \textbf{L}oR\textbf{A}), a novel \textbf{P}arameter \textbf{E}fficient \textbf{F}ine-\textbf{T}uning (PEFT) \textbf{M}ixture-\textbf{o}f-\textbf{E}xpert (MoE) paradigm for improved fine-tuning and parameter efficiency in multi-task learning. The paradigm effectively improves the multi-task capability of the model by training universal experts, domain-specific experts, and routers separately. MoDULA-Res is a new method within the MoDULA paradigm, which maintains the model's general capability by connecting universal and task-specific experts through residual connections. The experimental results demonstrate that the overall performance of the MoDULA-Flan and MoDULA-Res methods surpasses that of existing fine-tuning methods on various LLMs. Notably, MoDULA-Res achieves more significant performance improvements in multiple tasks while reducing training costs by over 80\% without losing general capability. Moreover, MoDULA displays flexible pluggability, allowing for the efficient addition of new tasks without retraining existing experts from scratch. This progressive training paradigm circumvents data balancing issues, enhancing training efficiency and model stability. Overall, MoDULA provides a scalable, cost-effective solution for fine-tuning LLMs with enhanced parameter efficiency and generalization capability.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The growing demand for larger-scale models in the development of \textbf{L}arge \textbf{L}anguage \textbf{M}odels (LLMs) poses challenges for efficient training within limited computational resources. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | Traditional fine-tuning methods often exhibit instability in multi-task learning and rely heavily on extensive training resources. | background 0.77 | 0.06 | 0.02 | asserted 0.99 |
| s2 | Here, we propose MoDULA (\textbf{M}ixture \textbf{o}f \textbf{D}omain-Specific and \textbf{U}niversal \textbf{L}oR\textbf{A}), a novel \textbf{P}arameter \textbf{E}fficient \textbf{F}ine-\textbf{T}uning (PEFT) \textbf{M}ixture-\textbf{o}f-\textbf{E}xpert (MoE) paradigm for improved fine-tuning and parameter efficiency in multi-task learning. | contribution 0.97 | 0.76 | 0.15 | asserted 0.96 |
| s3 | The paradigm effectively improves the multi-task capability of the model by training universal experts, domain-specific experts, and routers separately. | method 0.97 | 0.63 | 0.10 | asserted 1.00 |
| s4 | MoDULA-Res is a new method within the MoDULA paradigm, which maintains the model's general capability by connecting universal and task-specific experts through residual connections. | method 0.73 | 0.75 | 0.05 | asserted 1.00 |
| s5 | The experimental results demonstrate that the overall performance of the MoDULA-Flan and MoDULA-Res methods surpasses that of existing fine-tuning methods on various LLMs. | result 1.00 | 0.94 | 0.34 | asserted 1.00 |
| s6 | Notably, MoDULA-Res achieves more significant performance improvements in multiple tasks while reducing training costs by over 80\% without losing general capability. | result 1.00 | 0.95 | 0.32 | asserted 1.00 |
| s7 | Moreover, MoDULA displays flexible pluggability, allowing for the efficient addition of new tasks without retraining existing experts from scratch. | contribution 0.53 | 0.76 | 0.07 | asserted 1.00 |
| s8 | This progressive training paradigm circumvents data balancing issues, enhancing training efficiency and model stability. | result 0.31 | 0.68 | 0.06 | asserted 1.00 |
| s9 | Overall, MoDULA provides a scalable, cost-effective solution for fine-tuning LLMs with enhanced parameter efficiency and generalization capability. | interpretation 0.88 | 0.64 | 0.82 | asserted 1.00 |
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The paper
Original abstract
The growing demand for larger-scale models in the development of \textbf{L}arge \textbf{L}anguage \textbf{M}odels (LLMs) poses challenges for efficient training within limited computational resources. Traditional fine-tuning methods often exhibit instability in multi-task learning and rely heavily on extensive training resources. Here, we propose MoDULA (\textbf{M}ixture \textbf{o}f \textbf{D}omain-Specific and \textbf{U}niversal \textbf{L}oR\textbf{A}), a novel \textbf{P}arameter \textbf{E}fficient \textbf{F}ine-\textbf{T}uning (PEFT) \textbf{M}ixture-\textbf{o}f-\textbf{E}xpert (MoE) paradigm for improved fine-tuning and parameter efficiency in multi-task learning. The paradigm effectively improves the multi-task capability of the model by training universal experts, domain-specific experts, and routers separately. MoDULA-Res is a new method within the MoDULA paradigm, which maintains the model's general capability by connecting universal and task-specific experts through residual connections. The experimental results demonstrate that the overall performance of the MoDULA-Flan and MoDULA-Res methods surpasses that of existing fine-tuning methods on various LLMs. Notably, MoDULA-Res achieves more significant performance improvements in multiple tasks while reducing training costs by over 80\% without losing general capability. Moreover, MoDULA displays flexible pluggability, allowing for the efficient addition of new tasks without retraining existing experts from scratch. This progressive training paradigm circumvents data balancing issues, enhancing training efficiency and model stability. Overall, MoDULA provides a scalable, cost-effective solution for fine-tuning LLMs with enhanced parameter efficiency and generalization capability.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Cites it as background 1 paper
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Combining visual and textual information, they have made significant breakthroughs on numerous benchmark tests [ref] and are widely applied in fields like image captioning [ref] and visual question answering [ref] .
Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models · Introduction · 0.89
What it cites · 20 sentences without a confident label
Uses data or values from 2 papers
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To equip our and with comprehensive capabilities across universal, mathematical, coding, and medical domains, the datasets airoboros-3.2 [https://huggingface.co/datasets/jondurbin/airoboros-3.2], orca-math-word-problems-200k [https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k], CodeAlpaca-20k [https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k], and MedQA [ref] are integrated.
PubMedQA: A Dataset for Biomedical Research Question Answering · Training Datasets · 1.00
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To evaluate the pluggability and adaptability of different methods on new domain-specific tasks, we test their performance on the FinGPT-headline [ref] dataset from the finance domain, as well as the Title-Optimization and Keyword-Recommendation datasets from the e-commerce domain.
2306.06031 · Evaluation Benchmarks and Metrics · 0.93
Compares with 1 paper
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A detailed comparison is conducted among the standard LoRA [ref] , [ref] , and our newly proposed and .
LoRA: Low-Rank Adaptation of Large Language Models · Expert Configurations · 1.00
Cites as background 14 papers
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Researchers tried to address this with Parameter Efficient Fine-Tuning (PEFT), such as LoRA [ref] , Prefix Tuning [ref] , and $(IA)^3$ [ref] .
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning · Introduction · 0.85
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Researchers tried to address this with Parameter Efficient Fine-Tuning (PEFT), such as LoRA [ref] , Prefix Tuning [ref] , and $(IA)^3$ [ref] .
GPT Understands, Too · Introduction · 0.85
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Researchers tried to address this with Parameter Efficient Fine-Tuning (PEFT), such as LoRA [ref] , Prefix Tuning [ref] , and $(IA)^3$ [ref] .
LoRA: Low-Rank Adaptation of Large Language Models · Introduction · 0.85
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