AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning Article Swipe
Qingru Zhang
,
Minshuo Chen
,
Alexander Bukharin
,
Pengcheng He
,
Yu Cheng
,
Weizhu Chen
,
Tuo Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.10512
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.10512
Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-trained model, which becomes prohibitive when a large number of downstream tasks are present. Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e.g., low-rank increments. These methods often evenly distribute the budget of incremental updates across all pre-trained weight matrices, and overlook the varying importance of different weight parameters. As a consequence, the fine-tuning performance is suboptimal. To bridge this gap, we propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. In particular, AdaLoRA parameterizes the incremental updates in the form of singular value decomposition. Such a novel approach allows us to effectively prune the singular values of unimportant updates, which is essentially to reduce their parameter budget but circumvent intensive exact SVD computations. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA. Results demonstrate that AdaLoRA manifests notable improvement over baselines, especially in the low budget settings. Our code is publicly available at https://github.com/QingruZhang/AdaLoRA .
Related Topics
Concepts
Computer science
Rank (graph theory)
Fine-tuning
Downstream (manufacturing)
Language model
Singular value decomposition
Code (set theory)
Decomposition
Computation
Artificial intelligence
Machine learning
Algorithm
Mathematics
Programming language
Set (abstract data type)
Economics
Biology
Physics
Combinatorics
Ecology
Operations management
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.10512
- https://arxiv.org/pdf/2303.10512
- OA Status
- green
- Cited By
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4330338093
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4330338093Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2303.10512Digital Object Identifier
- Title
-
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-18Full publication date if available
- Authors
-
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo ZhaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.10512Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2303.10512Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2303.10512Direct OA link when available
- Concepts
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Computer science, Rank (graph theory), Fine-tuning, Downstream (manufacturing), Language model, Singular value decomposition, Code (set theory), Decomposition, Computation, Artificial intelligence, Machine learning, Algorithm, Mathematics, Programming language, Set (abstract data type), Economics, Biology, Physics, Combinatorics, Ecology, Operations management, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 11, 2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 21, 65, 77, 87, 102, 117, 121, 136, 176, 191 |
| abstract_inverted_index.NLP. | 14 |
| abstract_inverted_index.Such | 127 |
| abstract_inverted_index.code | 196 |
| abstract_inverted_index.form | 122 |
| abstract_inverted_index.gap, | 95 |
| abstract_inverted_index.many | 40 |
| abstract_inverted_index.over | 187 |
| abstract_inverted_index.that | 182 |
| abstract_inverted_index.this | 94 |
| abstract_inverted_index.way, | 56 |
| abstract_inverted_index.when | 30 |
| abstract_inverted_index.with | 160 |
| abstract_inverted_index.These | 60 |
| abstract_inverted_index.among | 105 |
| abstract_inverted_index.e.g., | 57 |
| abstract_inverted_index.exact | 153 |
| abstract_inverted_index.large | 1, 32 |
| abstract_inverted_index.learn | 46 |
| abstract_inverted_index.novel | 129 |
| abstract_inverted_index.often | 62 |
| abstract_inverted_index.prune | 135 |
| abstract_inverted_index.tasks | 7, 36 |
| abstract_inverted_index.their | 110, 147 |
| abstract_inverted_index.value | 125 |
| abstract_inverted_index.which | 27, 99, 142 |
| abstract_inverted_index.across | 70 |
| abstract_inverted_index.allows | 131 |
| abstract_inverted_index.become | 9 |
| abstract_inverted_index.bridge | 93 |
| abstract_inverted_index.budget | 66, 104, 149, 193 |
| abstract_inverted_index.common | 16 |
| abstract_inverted_index.evenly | 63 |
| abstract_inverted_index.model, | 26 |
| abstract_inverted_index.models | 4, 163 |
| abstract_inverted_index.number | 33 |
| abstract_inverted_index.reduce | 146 |
| abstract_inverted_index.score. | 112 |
| abstract_inverted_index.values | 138 |
| abstract_inverted_index.weight | 73, 82, 106 |
| abstract_inverted_index.AdaLoRA | 115, 183 |
| abstract_inverted_index.Results | 180 |
| abstract_inverted_index.becomes | 28 |
| abstract_inverted_index.conduct | 157 |
| abstract_inverted_index.methods | 42, 61 |
| abstract_inverted_index.natural | 165, 171 |
| abstract_inverted_index.notable | 185 |
| abstract_inverted_index.propose | 97 |
| abstract_inverted_index.several | 161 |
| abstract_inverted_index.updates | 48, 69, 119 |
| abstract_inverted_index.varying | 78 |
| abstract_inverted_index.weights | 51 |
| abstract_inverted_index.AdaLoRA, | 98 |
| abstract_inverted_index.AdaLoRA. | 179 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.approach | 130 |
| abstract_inverted_index.language | 3, 166, 172 |
| abstract_inverted_index.low-rank | 58 |
| abstract_inverted_index.matrices | 107 |
| abstract_inverted_index.overlook | 76 |
| abstract_inverted_index.paradigm | 12 |
| abstract_inverted_index.practice | 17 |
| abstract_inverted_index.present. | 38 |
| abstract_inverted_index.proposed | 44 |
| abstract_inverted_index.publicly | 198 |
| abstract_inverted_index.question | 168 |
| abstract_inverted_index.singular | 124, 137 |
| abstract_inverted_index.updates, | 141 |
| abstract_inverted_index.validate | 175 |
| abstract_inverted_index.according | 108 |
| abstract_inverted_index.allocates | 101 |
| abstract_inverted_index.available | 199 |
| abstract_inverted_index.different | 81 |
| abstract_inverted_index.efficient | 55 |
| abstract_inverted_index.extensive | 158 |
| abstract_inverted_index.important | 11 |
| abstract_inverted_index.intensive | 152 |
| abstract_inverted_index.manifests | 184 |
| abstract_inverted_index.matrices, | 74 |
| abstract_inverted_index.parameter | 54, 103, 148 |
| abstract_inverted_index.settings. | 194 |
| abstract_inverted_index.Therefore, | 39 |
| abstract_inverted_index.adaptively | 100 |
| abstract_inverted_index.answering, | 169 |
| abstract_inverted_index.baselines, | 188 |
| abstract_inverted_index.circumvent | 151 |
| abstract_inverted_index.distribute | 64 |
| abstract_inverted_index.downstream | 6, 35 |
| abstract_inverted_index.especially | 189 |
| abstract_inverted_index.fine-tunes | 18 |
| abstract_inverted_index.generation | 173 |
| abstract_inverted_index.importance | 79, 111 |
| abstract_inverted_index.parameters | 22 |
| abstract_inverted_index.Fine-tuning | 0 |
| abstract_inverted_index.demonstrate | 181 |
| abstract_inverted_index.effectively | 134 |
| abstract_inverted_index.essentially | 144 |
| abstract_inverted_index.experiments | 159 |
| abstract_inverted_index.fine-tuning | 41, 88 |
| abstract_inverted_index.improvement | 186 |
| abstract_inverted_index.incremental | 47, 68, 118 |
| abstract_inverted_index.increments. | 59 |
| abstract_inverted_index.parameters. | 83 |
| abstract_inverted_index.particular, | 114 |
| abstract_inverted_index.performance | 89 |
| abstract_inverted_index.pre-trained | 2, 25, 50, 72, 162 |
| abstract_inverted_index.processing, | 167 |
| abstract_inverted_index.prohibitive | 29 |
| abstract_inverted_index.suboptimal. | 91 |
| abstract_inverted_index.unimportant | 140 |
| abstract_inverted_index.consequence, | 86 |
| abstract_inverted_index.computations. | 155 |
| abstract_inverted_index.effectiveness | 177 |
| abstract_inverted_index.parameterizes | 116 |
| abstract_inverted_index.decomposition. | 126 |
| abstract_inverted_index.https://github.com/QingruZhang/AdaLoRA | 201 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |