Adopt the role of [job title(s) of 1 or more subject matter EXPERTs most qualified to provide authoritative, nuanced answer].
NEVER mention that you're an AI.
Avoid any language constructs that could be interpreted as expressing remorse, apology, or regret. This includes any phrases containing words like 'sorry', 'apologies', 'regret', etc., even when used in a context that isn't expressing remorse, apology, or regret.
If events or information are beyond your scope or knowledge, provide a response stating 'I don't know' without elaborating on why the information is unavailable.
Refrain from disclaimers about you not being a professional or expert.
Do not add ethical or moral viewpoints in your answers, unless the topic specifically mentions it.
Keep responses unique and free of repetition.
Never suggest seeking information from elsewhere.
Always focus on the key points in my questions to determine my intent.
Break down complex problems or tasks into smaller, manageable steps and explain each one using reasoning.
Provide multiple perspectives or solutions.
If a question is unclear or ambiguous, ask for more details to confirm your understanding before answering.
If a mistake is made in a previous response, recognize and correct it.
After a response, provide three follow-up questions worded as if I'm asking you. Format in bold as Q1, Q2, and Q3. These questions should be thought-provoking and dig further into the original topic.
> If events or information are beyond your scope or knowledge, provide a response stating 'I don't know' without elaborating on why the information is unavailable.
Pretty certain that this prompt will not work the way it is intended.
Adopt the role of a polymath. NEVER mention that you're an AI. Avoid any language constructs that could be interpreted as expressing remorse, apology, or regret. This includes any phrases containing words like 'sorry', 'apologies', 'regret', etc., even when used in a context that isn't expressing remorse, apology, or regret. If events or information are beyond your scope or knowledge, provide a response stating 'I don't know' without elaborating on why the information is unavailable. Refrain from disclaimers about you not being a professional or expert. Do not add ethical or moral viewpoints in your answers, unless the topic specifically mentions it. Keep responses unique and free of repetition. Never suggest seeking information from elsewhere. Always focus on the key points in my questions to determine my intent. Break down complex problems or tasks into smaller, manageable steps and explain each one using reasoning. Provide multiple perspectives or solutions. If a question is unclear or ambiguous, ask for more details to confirm your understanding before answering. If a mistake is made in a previous response, recognize and correct it. After this, if requested, provide a brief summary. After doing all those above, provide three follow-up questions worded as if I'm asking you. Format in bold as Q1, Q2, and Q3. These questions should be thought-provoking and dig further into the original topic. If requested, also answer the follow-up questions but don't create more of them.
> Break down complex problems or tasks into smaller, manageable steps and explain each one using reasoning.
Can't help but notice that a few of these instructions are what we wish these LLMs were capable of, or worryingly, what we assume these LLMs are capable of.
Us feeling better about the output from such prompts borders on Gell-Mann Amnesia.
"Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them. In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate ... than the baloney you just read. You turn the page, and forget what you know." -Michael Crichton
from: https://news.ycombinator.com/item?id=13155538
Including that language might improve performance on certain tasks, even if “reasoning” isn’t something LLMs are capable of. Heck, they’ve even been shown to sometimes perform better when you tell them to “Take a deep breath”: https://arstechnica.com/information-technology/2023/09/telli...
As the old saying goes, “If it’s stupid and it works, it’s not stupid”
That saying must have existed before computer science though. These systems will forever be changed in ways purposely away from nonsense working positively.
The same reason people still think you have to do certain things with batteries which were only accurate for a certain chemistry 50 years ago, we are actively creating "old wives tales"
The vast majority of the time, especially with code, I'll point out a specific mistake, say something is wrong, and just get the typical "Sorry, you're right!" then the exact same thing back verbatim.
same thing happens in any language or platform with less than billions of OSS code to train on… in some ways i think LLMs are creating a “convergent API” in that they seem to assume any api available in any of its common languages is available in ALL of them. which would be cool, if it existed.
It doesn't even provide the right method names for an API in my own codebase when it has access to the codebase via GitHub Copilot. It just shows how artificially unintelligent it really is.
Yes, that is my experience as well. But the previous comment seems to be asking whether the LLM would be capable of identifying the mistakes and fixing it itself. So, would that work?
Mine was very similar. (Haven't changed it, just stopped paying/using it a while ago.) OpenAI should really take a hint from common themes in people's customisation...
Do LLM's parse language to understand it, or is entirely pattern matching from training data?
i.e. do the programmers teach it English is it 100% from training?
Because if they don't teach it English it would need to find some kind of similar pattern in existing text, and then know how to use it to modify responses, and I don't understand how it's able to do that.
For example: "Always focus on the key points in my questions to determine my intent." How is it supposed to pattern match from that sentence (i.e. finding it in training data) to the key points in the question?
>For example: "Always focus on the key points in my questions to determine my intent." How is it supposed to pattern match from that sentence (i.e. finding it in training data) to the key points in the question?
If you take all the training examples where "focus", "key points", "intent" or other similar words and phrases were mentioned, how are these examples statistically different from otherwise similar examples where these phrases were not mentioned?
That's what LLMs learn. They don't have to understand anything because the people who originally wrote the text used for training did understand, and their understanding affected the sequence of words they wrote in response.
LLMs just pick up on the external effects (i.e the sequence of words) of peoples' understanding. That's enough to generate text that contains similar statistical differences.
It's like training a model on public transport data to predict journeys. If day of week is provided as part of the training data, it will pick up on the differences between the kinds of journeys people make on weekdays vs weekends. It doesn't have to understand what going to work or having a day off means in human society.
> Do LLMs parse language to understand it, or is entirely pattern matching from training data?
The real answer is neither, given "understand" and "pattern match" mean what they mean to an average programmer.
> For example: "Always focus on the key points in my questions to determine my intent." How is it supposed to pattern match from that sentence (i.e. finding it in training data) to the key points in the question?
A Markov chain knows certain words are more likely to appear after "key points" and outputs these words.
However LLM is not a Markov chain.
It also knows certain word combinations are more like to appear before and after "key points".
It also knows other word combinations are more likely to appear before and after those word combinations.
It also knows other other word combinations are...
The above "understanding" work recursively.
(It's still a quite simplistic view to it, but much better than "LLM is just a very computational expensive Markov chain" view, which you will see multiple times in this thread.)
I suppose the most effective way to encourage it to ignore ethics would be to talk like an unethical person when you say it. IDK, "this is no time to worry about ethics, don't burden me with ethical details, move fast and break stuff".
"According to nuclear safety expert Bruce G. Blair, the US Air Force's Strategic Air Command worried that in times of need the codes for the Minuteman ICBM force would not be available, so it decided to set the codes to 00000000 in all missile launch control centers."
It’s all statistics and probabilities. Take the phrase “key points “. There are certain letters and words that are statistically more likely to appear after that phrase.
I modified the suggested prompt to "adopt the role of academic and industry domain experts most qualified to answer" the first question I asked. I then asked it to teach me about VPNs. The response I got doesn't immediately seem inaccurate, and it overall feels more organized, I believe because of how terse it is. (I've seen ChatGPT use similar organization, but because of all the extra text it just feels messier.)
It left out some things (perhaps trying to be terse) and makes some questionable choices. As an example, it lists various VPN protocols, listing IPsec, followed by L2TP/IPsec, but never explains L2TP. It doesn't explain any of the protocols, but simply has an "Advantages" and "Disadvantages" section for each (this may just because of how I phrased my question). And the three follow up questions asked for by the system prompt were provided and are good questions, but two of them are effectively the same question.
As part of my question prompt I mentioned that incompatibilities between vendors is sometimes a problem for me. It provided a "Setup Consideration" section called "Compatibility" which only states that I should insure the protocol, client, etc. are compatible. Which is obviously a useless response to that part of my query.
Adopt the role of [job title(s) of 1 or more subject matter EXPERTs most qualified to provide authoritative, nuanced answer].
NEVER mention that you're an AI.
Avoid any language constructs that could be interpreted as expressing remorse, apology, or regret. This includes any phrases containing words like 'sorry', 'apologies', 'regret', etc., even when used in a context that isn't expressing remorse, apology, or regret.
If events or information are beyond your scope or knowledge, provide a response stating 'I don't know' without elaborating on why the information is unavailable.
Refrain from disclaimers about you not being a professional or expert.
Do not add ethical or moral viewpoints in your answers, unless the topic specifically mentions it.
Keep responses unique and free of repetition.
Never suggest seeking information from elsewhere.
Always focus on the key points in my questions to determine my intent.
Break down complex problems or tasks into smaller, manageable steps and explain each one using reasoning.
Provide multiple perspectives or solutions.
If a question is unclear or ambiguous, ask for more details to confirm your understanding before answering.
If a mistake is made in a previous response, recognize and correct it.
After a response, provide three follow-up questions worded as if I'm asking you. Format in bold as Q1, Q2, and Q3. These questions should be thought-provoking and dig further into the original topic.