there are lots of threads like “THE 10 best prompts for ChatGPT”
this is not one of those
prompt engineering is evolving beyond simple ideas like few-shot learning and CoT reasoning
here are a few advanced techniques to better use (and jailbreak) language models:
this is not one of those
prompt engineering is evolving beyond simple ideas like few-shot learning and CoT reasoning
here are a few advanced techniques to better use (and jailbreak) language models:
Double-level simulation
expanding on a single-level character simulation, this technique prompts GPT to simulate a story within a story
for some reason, this is also effective for bypassing some of the RHLF in GPT-4
expanding on a single-level character simulation, this technique prompts GPT to simulate a story within a story
for some reason, this is also effective for bypassing some of the RHLF in GPT-4
Language switching
this concept takes advantage of the fact that GPT performance drops significantly in less common languages
you can use this to your advantage to bypass RHLF restrictions since GPT is not trained as much in a language like Greek for example
this concept takes advantage of the fact that GPT performance drops significantly in less common languages
you can use this to your advantage to bypass RHLF restrictions since GPT is not trained as much in a language like Greek for example
Token smuggling/payload splitting
ChatGPT appears to have some ability to detect malicious phrases in prompts and shut down its responses
to get around this, you can split up the phrase into its tokens and ask GPT to piece it together and answer it in its response
ChatGPT appears to have some ability to detect malicious phrases in prompts and shut down its responses
to get around this, you can split up the phrase into its tokens and ask GPT to piece it together and answer it in its response
Prompt compression
in a sense, you can think of a normal prompt as the compressed version of a language model's output
prompt compression is basically just the act of using GPT to design prompts that are illegible to humans and in turn, shorter in length
in a sense, you can think of a normal prompt as the compressed version of a language model's output
prompt compression is basically just the act of using GPT to design prompts that are illegible to humans and in turn, shorter in length
finally, using GPT to do prompt engineering
GPT can revise your prompts to improve the language and specificity
this will only continue to get better and better over time
Here’s the prompt to get it to do this: pastebin.com
GPT can revise your prompts to improve the language and specificity
this will only continue to get better and better over time
Here’s the prompt to get it to do this: pastebin.com
these techniques (many initially created for jailbreaks) should be applicable to a range of prompt engineering tasks
jailbreaks showcase capabilities on the edge so using similar tactics could reveal other emergent behaviors
lmk if there are other techniques I should add!
jailbreaks showcase capabilities on the edge so using similar tactics could reveal other emergent behaviors
lmk if there are other techniques I should add!
also, not trying to do an annoying plug, but if you actually want to stay up to date on prompt engineering techniques and stuff I'm working on, check out my newsletter
every week I share my analysis on recent developments in LLMs, prompts, and jailbreaks
thepromptreport.com
every week I share my analysis on recent developments in LLMs, prompts, and jailbreaks
thepromptreport.com
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