Semanlink - Prompting, Instruction Finetuning, and RLHF (CS224N)

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sl:bookmarkOf : https://web.stanford.edu/class/cs224n/slides/cs224n-2023-lecture11-prompting-rlhf.pdf
sl:creationDate : 2023-02-16
sl:creationTime : 2023-02-16T23:12:04Z

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Bookmark of: https://web.stanford.edu/class/cs224n/slides/cs224n-2023-lecture11-prompting-rlhf.pdf

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> There turned out to be a phase shift somewhere between 60B parameters and 175B parameters, that made language models super impressive.

> **The performance of current days language models are not obtained by language modeling**
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>    - [Traditional] LMs are not [grounded](tag:grounded_language_learning)
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> Instruction tuning: For example, the human annotators would write something like "please summarize this text", followed by some text they got, followed by a summary they produced of this text. -> Some symbols ("summarize", "translate", "formal") are used in a consistent way together with the concept/task they denote. And they always appear in the beginning of the text. -> the act of producing a summary grounded to the human concept of "summary"
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> code: programming language code data, and specifically data that contains both natural language instructions or descriptions (in the form of code comments) and the corresponding programming language code. This produced another very direct form of grounding. the human language describes concepts (or intents), which are then realized in the form of the corresponding programs.
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