Automated Few-shot Classification with Instruction-Finetuned Language Models Article Swipe
Rami Aly
,
Xingjian Shi
,
Kaixiang Lin
,
Aston Zhang
,
Andrew Gordon Wilson
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.12576
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.12576
A particularly successful class of approaches for few-shot learning combines language models with prompts -- hand-crafted task descriptions that complement data samples. However, designing prompts by hand for each task commonly requires domain knowledge and substantial guesswork. We observe, in the context of classification tasks, that instruction finetuned language models exhibit remarkable prompt robustness, and we subsequently propose a simple method to eliminate the need for handcrafted prompts, named AuT-Few. This approach consists of (i) a prompt retrieval module that selects suitable task instructions from the instruction-tuning knowledge base, and (ii) the generation of two distinct, semantically meaningful, class descriptions and a selection mechanism via cross-validation. Over $12$ datasets, spanning $8$ classification tasks, we show that AuT-Few outperforms current state-of-the-art few-shot learning methods. Moreover, AuT-Few is the best ranking method across datasets on the RAFT few-shot benchmark. Notably, these results are achieved without task-specific handcrafted prompts on unseen tasks.
Related Topics
Concepts
Computer science
Artificial intelligence
Benchmark (surveying)
Task (project management)
Heuristics
Robustness (evolution)
Class (philosophy)
Natural language processing
Language model
Context (archaeology)
Machine learning
Chemistry
Management
Paleontology
Geography
Operating system
Biochemistry
Economics
Gene
Geodesy
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.12576
- https://arxiv.org/pdf/2305.12576
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4377864503
All OpenAlex metadata
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https://openalex.org/W4377864503Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.12576Digital Object Identifier
- Title
-
Automated Few-shot Classification with Instruction-Finetuned Language ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-05-21Full publication date if available
- Authors
-
Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon WilsonList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.12576Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.12576Direct 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/2305.12576Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Benchmark (surveying), Task (project management), Heuristics, Robustness (evolution), Class (philosophy), Natural language processing, Language model, Context (archaeology), Machine learning, Chemistry, Management, Paleontology, Geography, Operating system, Biochemistry, Economics, Gene, Geodesy, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.samples. | 21 |
| abstract_inverted_index.spanning | 109 |
| abstract_inverted_index.suitable | 81 |
| abstract_inverted_index.Moreover, | 123 |
| abstract_inverted_index.datasets, | 108 |
| abstract_inverted_index.designing | 23 |
| abstract_inverted_index.distinct, | 95 |
| abstract_inverted_index.eliminate | 62 |
| abstract_inverted_index.finetuned | 47 |
| abstract_inverted_index.knowledge | 33, 87 |
| abstract_inverted_index.mechanism | 103 |
| abstract_inverted_index.retrieval | 77 |
| abstract_inverted_index.selection | 102 |
| abstract_inverted_index.approaches | 5 |
| abstract_inverted_index.benchmark. | 136 |
| abstract_inverted_index.complement | 19 |
| abstract_inverted_index.generation | 92 |
| abstract_inverted_index.guesswork. | 36 |
| abstract_inverted_index.remarkable | 51 |
| abstract_inverted_index.successful | 2 |
| abstract_inverted_index.handcrafted | 66, 144 |
| abstract_inverted_index.instruction | 46 |
| abstract_inverted_index.meaningful, | 97 |
| abstract_inverted_index.outperforms | 117 |
| abstract_inverted_index.robustness, | 53 |
| abstract_inverted_index.substantial | 35 |
| abstract_inverted_index.descriptions | 17, 99 |
| abstract_inverted_index.hand-crafted | 15 |
| abstract_inverted_index.instructions | 83 |
| abstract_inverted_index.particularly | 1 |
| abstract_inverted_index.semantically | 96 |
| abstract_inverted_index.subsequently | 56 |
| abstract_inverted_index.task-specific | 143 |
| abstract_inverted_index.classification | 43, 111 |
| abstract_inverted_index.state-of-the-art | 119 |
| abstract_inverted_index.cross-validation. | 105 |
| abstract_inverted_index.instruction-tuning | 86 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |