Model Evaluation and Threat Research is an AI research charity that looks into the threat of AI agents! That sounds a bit AI doomsday cult, and they take funding from the AI doomsday cult organisat…
I talked to Microsoft Copilot 3 times for work related reasons because I couldn’t find something in documentation. I was lied to 3 times. It either made stuff up about how the thing I asked about works or even invented entirely new configuration settings
Claude AI does this ALL the time too. It NEEDS to give a solution, it rarely can say “I don’t know” so it will just completely make up a solution that it thinks is right without actually checking to see the solution exists. It will make/dream up programs or libraries that don’t and have never existed OR it will tell you something can do something when it has never been able to do that thing ever.
And that’s just how all these LLMs have been built. they MUST provide a solution so they all lie. they’ve been programmed this way to ensure maximum profits. Github Copilot is a bit better because it’s with me in my code so it’s suggestions, most of the time, actually work because it can see the context and whats around it. Claude is absolute garbage, MS Copilot is about the same caliber if not worse than Claude, and Chatgpt is only good for content writing or bouncing ideas off of.
Are you using Claude web chat or Claude code? Because my experience with it is vastly different eve when using the same underlying model. Clause code isn’t perfect and gets stuff wrong, but it can run the project check the output and realize it’s mistake and fix it in many cases. It doesn’t fix logic flaws, but it can fix hallucinations of library methods that don’t exist.
LLM are just sophisticated text predictions engine. They don’t know anything, so they can’t produce an “I don’t know” because they can always generate a text prediction and they can’t think.
They could be programmed to do some double/triple checking, and return “i dont know” when the checks are negative.
I guess that would compromise the apparence of oracle that their parent companies seem to dissimulately push onto them.
Tool use, reasoning, chain of thought, those are the things that set llm systems apart. While you are correct in the most basic sense, it’s like saying a car is only a platform with wheels, it’s reductive of the capabilities
In fairness the msdn documentation is prone to this also.
By “this” I mean having what looks like a comprehensive section about the thing you want but the actual information you need isn’t there, but you need to tag the whole thing to find out.
I talked to Microsoft Copilot 3 times for work related reasons because I couldn’t find something in documentation. I was lied to 3 times. It either made stuff up about how the thing I asked about works or even invented entirely new configuration settings
Claude AI does this ALL the time too. It NEEDS to give a solution, it rarely can say “I don’t know” so it will just completely make up a solution that it thinks is right without actually checking to see the solution exists. It will make/dream up programs or libraries that don’t and have never existed OR it will tell you something can do something when it has never been able to do that thing ever.
And that’s just how all these LLMs have been built. they MUST provide a solution so they all lie. they’ve been programmed this way to ensure maximum profits. Github Copilot is a bit better because it’s with me in my code so it’s suggestions, most of the time, actually work because it can see the context and whats around it. Claude is absolute garbage, MS Copilot is about the same caliber if not worse than Claude, and Chatgpt is only good for content writing or bouncing ideas off of.
Are you using Claude web chat or Claude code? Because my experience with it is vastly different eve when using the same underlying model. Clause code isn’t perfect and gets stuff wrong, but it can run the project check the output and realize it’s mistake and fix it in many cases. It doesn’t fix logic flaws, but it can fix hallucinations of library methods that don’t exist.
LLM are just sophisticated text predictions engine. They don’t know anything, so they can’t produce an “I don’t know” because they can always generate a text prediction and they can’t think.
They could be programmed to do some double/triple checking, and return “i dont know” when the checks are negative. I guess that would compromise the apparence of oracle that their parent companies seem to dissimulately push onto them.
Tool use, reasoning, chain of thought, those are the things that set llm systems apart. While you are correct in the most basic sense, it’s like saying a car is only a platform with wheels, it’s reductive of the capabilities
LLM are prediction engine. They don’t have knowledge, they only chain words together related to your topic.
They don’t know they are wrong because they just don’t know anything period.
They have a point, chatbots are built on top of LLMs, they arent just LLMs.
In fairness the msdn documentation is prone to this also.
By “this” I mean having what looks like a comprehensive section about the thing you want but the actual information you need isn’t there, but you need to tag the whole thing to find out.