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(Page créée avec « [1] The in-context learning (ICL) ability is formally introduced by GPT-3 : assuming that the language model has been provided with a natural language instruction and/or several task demonstrations, it can generate the expected output for the test instances by completing the word sequence of input text, without requiring additional training or gradient update == Références == * [https://arxiv.org/pdf/2303.18223.pdf] A Survey of Large Language Models ») |
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[1] The in-context learning (ICL) ability is formally introduced by GPT-3 : assuming that the language model has been provided with a natural language instruction and/or several task demonstrations, it can generate the expected output for the test instances by completing the word sequence of input text, without requiring additional training or gradient update | [1] The in-context learning (ICL) ability is formally introduced by GPT-3 : assuming that the language model has been provided with a natural language instruction and/or several task demonstrations, it can generate the expected output for the test instances by completing the word sequence of input text, without requiring additional training or gradient update | ||
[[File:A comparative illustration of in-context learning (ICL) and chain-of-thought (CoT) prompting.jpg|500px]] | |||
== Références == | == Références == | ||
* [https://arxiv.org/pdf/2303.18223.pdf] A Survey of Large Language Models | * [https://arxiv.org/pdf/2303.18223.pdf] A Survey of Large Language Models | ||
Version du 27 avril 2023 à 16:38
[1] The in-context learning (ICL) ability is formally introduced by GPT-3 : assuming that the language model has been provided with a natural language instruction and/or several task demonstrations, it can generate the expected output for the test instances by completing the word sequence of input text, without requiring additional training or gradient update
_and_chain-of-thought_(CoT)_prompting.jpg){kind=link}
Références
- [1] A Survey of Large Language Models