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Muppet: Massive Multi-task Representations with Pre-Finetuning
Open accessAbstract
We propose pre-finetuning, an additional largescale learning stage between language model pre-training and fine-tuning.Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks.We show that prefinetuning consistently improves performance for pretrained discriminators (e.g.RoBERTa) and generation models (e.g.BART) on a wide range of tasks (sentence prediction, commonsense reasoning, MRC, etc.), while also significantly improving sample efficiency during fine-tuning.We also show that large-scale multi-tasking is crucial; pre-finetuning can hurt performance when few tasks are used up until a critical point (usually above 15) after which performance improves linearly in the number of tasks.
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Self-citation
Metadata
- Authors
- Aghajanyan, A., Gupta, A., Shrivastava, A., Chen, X., Zettlemoyer, L., & Gupta, S.
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 5799-5811
- OpenAlex
- W3124687886
- Cited by
- 183
- References
- 82
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 23 · 2023: 64 · 2022: 55 · 2021: 13
- FWCI
- 17.90
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- multi-task learning, representation learning, language model pretraining, fine-tuning, sample efficiency, RoBERTa, BART, commonsense reasoning, machine reading comprehension, sentence prediction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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