In-context learning - Historique des versions

Jboscher le 27 avril 2023 à 16:40

2023-04-27T16:40:37Z

← Version précédente		Version du 27 avril 2023 à 16:40
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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:~~Capture d’écran 2023~~-04-~~27 à 18.32.50~~.~~png~~\|500px]]		[[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

Jboscher le 27 avril 2023 à 16:40

2023-04-27T16:40:02Z

← Version précédente		Version du 27 avril 2023 à 16:40
Ligne 2 :		Ligne 2 :
	[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]]		[[File:Capture d’écran 2023-04-27 à 18.32.50.png\|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

Jboscher le 27 avril 2023 à 16:38

2023-04-27T16:38:44Z

← Version précédente		Version du 27 avril 2023 à 16:38
Ligne 2 :		Ligne 2 :
	[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

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2023-04-27T16:35:13Z

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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

== Références ==

* [https://arxiv.org/pdf/2303.18223.pdf] A Survey of Large Language Models