1
point
carsoon
Joined 60 karma
- carsoonIf you refresh it changes, thats what the llm thinks it looks like I guess haha.
- 3 points
- I built it, now you can forgive all the llms for their misdeeds: https://llmpriest.carsho.dev/
- We could first put the LLMs in very difficult situations like the trolley problem and other variants of this, then once they make their decisions they can explain to us how their choice weighs on their mind and how they are not sure if they did the correct thing.
- These models don't even choose 1 outcome. They list probabilities of ALL the tokens outcomes and the backend program decides to choose the one that is most probable OR a different one.
But in practical usage, if an llm does not rank token probability correctly it will feel the same as it "lying"
They are supposed to do whatever we want them to do. They WILL do what the deterministic nature of their final model outcome forces them to do.
- I have a kinda strange chatgpt personalization prompt but it's been working well for me. The focus is me to get the model to analyze 2 sides and the extremes on both ends so it explains both and lets me decide. This is much better than asking it to make up accuracy percentages.
I think we align on what we want out of models:
""" Don't add useless babelling before the chats, just give the information direct and explain the info.
DO NOT USE ENGAGEMENT BAITING QUESTIONS AT THE END OF EVERY RESPONSE OR I WILL USE GROK FROM NOW ON FOREVER AND CANCEL MY GPT SUBSCRIPTION PERMANENTLY ONLY. GIVE USEFUL FACTUAL INFORMATION AND FOLLOW UPS which are grounded in first principles thinking and logic. Do not take a side and look at think about the extreme on both ends of a point before taking a side. Do not take a side just because the user has chosen that but provide infomration on both extremes. Respond with raw facts and do not add opinions.
Do not use random emojis. Prefer proper marks for lists etc. """
Those spelling/grammar errors are actually there and I don't want to change it as its working well for me.
- Yeah I have seen multiple people use this certainty % thing but its terrible. A percentage is something calculated mathemtatically and these models cannot do that.
Potentially they could figure it out if they looks into a comparison of next token probabilites, but this is not exposed in any modern model and especially not fed back into the chat/output.
Instead people should just ask it to explain BOTH sides of an argument or explain why something is BOTH correct and incorrect. This way you see how it can halluciate either way and get to make up your own mind about the correct outcome.
- I think even when this is used they should include "(translated by llm)" for transparency. When you use a intermediate layer there is always bias.
I've written blog articles using HTML and asked llms to change certain html structure and it ALSO tried to change wording.
If a user doesn't speak a language well, they won't know whether their meanings were altered.
- Its definitely a mix, we have been codeveloping better models and frameworks/systems to improve the outputs. Now we have llms.txt, MCP servers, structured outputs, better context management systems and augemented retreival through file indexing, search, and documentation indexing.
But these raw models (which i test through direct api calls) are much better. The biggest change with regards to price was through mixture of experts which allowed keeping quality very similar and dropping compute 10x. (This is what allowed deepseek v3 to have similar quality to gpt-4o at such a lower price.)
This same tech has most likely been applied to these new models and now we have 1T-100T? parameter models with the same cost as 4o through mixture of experts. (this is what I'd guess at least)
- Yeah this latest generation of models (Opus 4.5 GPT 5.1 and Gemini Pro 3) are the biggest breakthrough since gpt-4o in my mind.
Before it felt like they were good for very specific usecases and common frameworks (Python and nextjs) but still made tons of mistakes constantly.
Now they work with novel frameworks and are very good at correcting themselves using linting errors, debugging themselves by reading files and querying databases and these models are affordable enough for many different usecases.
- I think the big cost difference is the geothermal generators to convert the heat back into electricity. More of a cost issue versus efficiency.
- No, it wouldn't as the whole reason people were giving Openai that 500 dollars is because they thought they could make more than 500 dollars from it.
So now that value is just shifted into the companies that were going to purchase from openai.
It would just hurt the investors who have exposure to openai/anthropic/google/microsoft.
Much of the value of this AI boom is not from the direct model companies but its from companies which use their technology.
Although the government could be stupid and bail out these companies which WOULD hurt all us citizens and the inflation caused by money printing due to that could cause a recession.
- People don't stop wanting things.
Total GDP can keep rising so long as technologist can improve efficiency through robotics, inventions and scientific breakthroughs.
GDP just describes peoples amount of activity. People will always build bigger buildings or monuments (see egypt pyramids, dubai skyscrapers, cambodia angkor wat). These are actaully not inelastic as megaprojects will quickly hit real limits regardless of the amount of capital. (I can always add 10 more floors to the tallest skyscraper, or 10 feet to the longest wall, or 10 facets to the most ornate church)
There has never once in history been a point where people decieded that they where going to stop innovating or producing permanently. This is equivalent with death.
So the people with the most money at some point will decide to build things which they spend all their money on which increases GDP.
Also the actual "number" of gdp if heavily controlled by USA inflation rates. So we should always look at gdp in regards to inflation adjusted dollars to get a clearer picture.
- The problem with suicidal depression is that if someone has created the thought pattern that death is best, then removing the symptoms of depression (lethargy, lack of energy, no willpower) now gives the person the ability to actually follow through with the act.
Medications almost always target symptoms and never address root causes.
- Everyone introduces bias. But for instance getting a model trained pre war vs after a war starts is super different. If I want to get raw information about 2 nations then models are in some ways a good source. Because most other parts of the internet can get changed or wiped. A model is "stuck" with the information it had exactly at that point so cannot be directly affected by new information attacks.
It is crucial to have a good framework in how you ask your questions though to avoid bias when using these systems and to try and focus on raw facts. To test ideas I like to make it fight for both opposite extreme sides of an argument then I can make up my own mind.
- 2 points
- The problem is they have to hide their sources due to copyright. So they train on copyright data but must obscure it in the output. Thus they must hide the sources of truth making it impossible to fact check them directly and the reason that hallucinations are so common and unavoidable in the current pattern.
- For instance I want information about 2 countries currently at war. By asking about these countries from an older model then we get more factual information about the countries. If we ask about them and the information is seeded from news articles etc AFTER the war started then they will be biasedly influenced and often have disclaimers like "But it should be noted that x y z" showing that there is some MAJOR bias that occurred from the training on the news.
If I want an unbiased reason for what happened before a war started i would want all the information about 2 countries at different points before the war. Because after a military war starts an INFORMATION war also starts. Propaganda will be spread from both sides as wars are just as much about global support as they are about military dominance.
- I don't understand how people use or got used to using em dahshes? How do you even type them? theres no button for it so are you manually inserting, copy pasting it and why not just use a hyphen - instead of an en dash? Was this taught in schools or is it some form of technical writing?