Fine-Tuning Language Models via Epistemic Neural Networks Article Swipe
Ian Osband
,
Seyed Mohammad Asghari
,
Benjamin Van Roy
,
Nat McAleese
,
John Aslanides
,
Geoffrey Irving
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.01568
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.01568
Language models often pre-train on large unsupervised text corpora, then fine-tune on additional task-specific data. However, typical fine-tuning schemes do not prioritize the examples that they tune on. We show that, if you can prioritize informative training data, you can achieve better performance while using fewer labels. To do this we augment a language model with an epinet: a small additional network that helps to estimate model uncertainty and forms an \textit{epistemic neural network} (ENN). ENNs are neural networks that can know what they don't know. Using an epinet to prioritize uncertain data, we can fine-tune BERT on GLUE tasks to the same performance while using 2x less data than training without prioritization. We also investigate performance in synthetic neural network generative models designed to build understanding. In each setting, using an epinet outperforms heuristic active learning schemes.
Related Topics
Concepts
Computer science
Artificial neural network
Artificial intelligence
Task (project management)
Heuristic
Language model
Machine learning
Prioritization
Deep neural networks
Generative grammar
Training set
Language understanding
Generative model
Natural language processing
Management
Management science
Economics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.01568
- https://arxiv.org/pdf/2211.01568
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308243057
All OpenAlex metadata
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https://openalex.org/W4308243057Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.01568Digital Object Identifier
- Title
-
Fine-Tuning Language Models via Epistemic Neural NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-03Full publication date if available
- Authors
-
Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy, Nat McAleese, John Aslanides, Geoffrey IrvingList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.01568Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.01568Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2211.01568Direct OA link when available
- Concepts
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Computer science, Artificial neural network, Artificial intelligence, Task (project management), Heuristic, Language model, Machine learning, Prioritization, Deep neural networks, Generative grammar, Training set, Language understanding, Generative model, Natural language processing, Management, Management science, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.language | 53 |
| abstract_inverted_index.learning | 136 |
| abstract_inverted_index.networks | 78 |
| abstract_inverted_index.network} | 73 |
| abstract_inverted_index.schemes. | 137 |
| abstract_inverted_index.setting, | 129 |
| abstract_inverted_index.training | 36, 110 |
| abstract_inverted_index.fine-tune | 10, 95 |
| abstract_inverted_index.heuristic | 134 |
| abstract_inverted_index.pre-train | 3 |
| abstract_inverted_index.synthetic | 118 |
| abstract_inverted_index.uncertain | 91 |
| abstract_inverted_index.additional | 12, 60 |
| abstract_inverted_index.generative | 121 |
| abstract_inverted_index.prioritize | 21, 34, 90 |
| abstract_inverted_index.fine-tuning | 17 |
| abstract_inverted_index.informative | 35 |
| abstract_inverted_index.investigate | 115 |
| abstract_inverted_index.outperforms | 133 |
| abstract_inverted_index.performance | 42, 103, 116 |
| abstract_inverted_index.uncertainty | 67 |
| abstract_inverted_index.unsupervised | 6 |
| abstract_inverted_index.task-specific | 13 |
| abstract_inverted_index.understanding. | 126 |
| abstract_inverted_index.prioritization. | 112 |
| abstract_inverted_index.\textit{epistemic | 71 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |