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This is nonsense - a LLMs output is a pseudo random mishmash of its training data. How can there be a "best prompt" when the same prompt gives you different out

This is nonsense - a LLMs output is a pseudo random mishmash of its training data. How can there be a "best prompt" when the same prompt gives you different output each time, but there is only one correct output?

syntheweave 4y ago
Calibration of the response occurs through prompting. That is, if you ask GPT a question directly, it will give the most glib, unfiltered answer possible like an average Reddit user. But if you tell it to role-play a persona it will apply elements of that persona, causing it to filter itself. Or if you tell it "describe your reasoning step by step" it will proceed down a path with greater causality. Or if you tell it "here are the rules of a language, give a response to the prompt in this language" it will attempt to produce a response that looks like those rules, using the weighting associated with logical words like "must," "cannot," "if," etc. Measuring calibration is the problem now: we know some prompts do much better than others, but not how to optimize that in a general sense, to make an LLM always adopt the persona needed for the job.
fwlr 4y ago
Prompts constrain the possible output space. This is true in trivial ways: ask it to reply only in json. This is also true in slightly less trivial ways: ask for a “description of X”, a “short description of X”, and a “one-sentence description of X”. This continues to be true in increasingly more complex ways: Prompt it with a quote, a rating of that quote as 5/10 on complexity, and request it to give you two new quotes with ratings of 2/10 and 8/10 on complexity. Follow it up with a request for two more quotes at -5/10 and 20/10 ratings. Then try the whole process again but with metrics other than “complexity”: information content, eloquence, humour. In this way, if there is a single correct output, there is a corresponding single best input prompt that most tightly constrains the output space to the smallest space that still contains the correct output.

syntheweave 4y ago

Calibration of the response occurs through prompting. That is, if you ask GPT a question directly, it will give the most glib, unfiltered answer possible like an average Reddit user. But if you tell it to role-play a persona it will apply elements of that persona, causing it to filter itself. Or if you tell it "describe your reasoning step by step" it will proceed down a path with greater causality. Or if you tell it "here are the rules of a language, give a response to the prompt in this language" it will attempt to produce a response that looks like those rules, using the weighting associated with logical words like "must," "cannot," "if," etc. Measuring calibration is the problem now: we know some prompts do much better than others, but not how to optimize that in a general sense, to make an LLM always adopt the persona needed for the job.

fwlr 4y ago

Prompts constrain the possible output space. This is true in trivial ways: ask it to reply only in json. This is also true in slightly less trivial ways: ask for a “description of X”, a “short description of X”, and a “one-sentence description of X”. This continues to be true in increasingly more complex ways: Prompt it with a quote, a rating of that quote as 5/10 on complexity, and request it to give you two new quotes with ratings of 2/10 and 8/10 on complexity. Follow it up with a request for two more quotes at -5/10 and 20/10 ratings. Then try the whole process again but with metrics other than “complexity”: information content, eloquence, humour. In this way, if there is a single correct output, there is a corresponding single best input prompt that most tightly constrains the output space to the smallest space that still contains the correct output.