Comment by sdesol
2 days ago
> On a similar note, has anyone found themselves absolutely not trusting non-code LLM output?
I'm working on a LLM chat app that is built around mistrust. The basic idea is that it is unlikely a supermajority of quality LLMs can get it wrong.
This isn't foolproof though, but it does provide some level of confidence in the answer.
Here is a quick example in which I analyze results from multiple LLMs that answered, "When did Homer Simpson go to Mars?"
https://beta.gitsense.com/?chat=4d28f283-24f4-4657-89e0-5abf...
If you look at the yes and no table, all except GPT-4o and GPT-4o mini said no. After asking GPT-4o who was correct, it provided "evidence" on an episode so I asked for more information on that episode. Based on what it said, it looks like the mission to Mars was a hoax and when I challenged GPT-4o on this, it agreed and said Homer never went to Mars, like others have said.
I then asked Sonnet 3.5 about the episode and it said GPT-4o misinterpreted the plot.
https://beta.gitsense.com/?chat=4d28f283-24f4-4657-89e0-5abf...
At this point, I am confident (but not 100% sure) Homer never went to Mars and if I really needed to know, I'll need to search the web.
It's the backwards reasoning that really frustrates me when using LLMs. You ask a question, it says sure do these things, they don't work out and you ask the LLM why not, and it replies yes that thing I told you to do wouldn't work because of these clear reasons.
It would be nice to start at the end of that chain of reasoning instead of the other side.
Another regular example is when it "invents" functions or classes that don't exist, when pressed about them, it will reply of course that won't work, that function doesn't exist.
Okay great, so don't tell me it does with such certainty, is what I would tell a human feeding me imagination as facts all the time. But of course an LLM is not reasoning in the same sense, so this reverse chain of thought is the outcome.
I am finding LLMs far more useful for soft skill topics than engineering type work, simply because of how often it leads me down a path that is eventually a dead end, because of some small detail that was wrong at the very beginning.
> I am finding LLMs far more useful for soft skill topics than engineering type work, simply because of how often it leads me down a path that is eventually a dead end, because of some small detail that was wrong at the very beginning.
Yeah I felt the same way in the beginning which is why I ended up writing my own chat app. What I've found while developing my spelling and grammar checker is that it is very unlikely for multiple LLMs to mess up at the same time. I know they will mess up, but I'm also pretty sure they won't at the same time.
So far, I've been able to successfully create working features that actually saved me time by pitting LLMs against their own responses and others. My process right now is, I'll ask 6+ models to implement something and then I will ask models to evaluate everyone's responses. More often than not, a model will find fault or make a suggestion that can be used to improve the prompt or code. And depending on my confidence level, I might repeat this a couple of times.
The issue right now is tracking this "chain of questioning" which is why I am writing my own chat app. I need an easy way to backtrack and fork from different points in the "chain of questioning". I think once we get a better understanding of what LLMs can and can't do as a group, we should be able to produce working solutions easier.
I'm imagining a sort of tree-like or graph like interface where each subsequent prompting by the user shows each LLM's answers below it connected by lines
I believe that this is what chain of thought models attempt to address.
Isn't this essentially making the point of the post above you?
For comparison - if I just do a web search for "Did homer simpson go to mars" I get immediately linked to the wikipedia page for that exact episode (https://en.wikipedia.org/wiki/The_Marge-ian_Chronicles), and the plot summary is less to read than your LLM output - It clearly summarizes that Marge & Lisa (note - NOT homer) almost went to mars, but did not go. Further - the summary correctly includes the outro which does show Marge and Lisa on mars in the year 2051.
Basically - for factual content, the LLM output was a garbage game of telephone.
> Isn't this essentially making the point of the post above you?
Yes. This is why I wrote the chat app, because I mistrust LLMs, but I do find them extremely useful when you approach them with the right mindset. If answering "Did Homer Simpson go to Mars?" correctly is critical, then you can choose to require a 100% consensus, otherwise you will need a fallback plan.
When I asked all the LLMs about the Wikipedia article, they all correctly answered "No" and talked about Marge and Lisa in the future without Homer.
Relatedly, asking LLMs what happens in a TV episode, or a series in general, I usually get very low quality and mostly flat out wrong answers. That baffles me, as I thought there are multiple well structured synopses for any TV series in the training data.