Selfhosted
A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don't control.
Rules:
-
Be civil.
-
No spam.
-
Posts are to be related to self-hosting.
-
Don't duplicate the full text of your blog or readme if you're providing a link.
-
Submission headline should match the article title.
-
No trolling.
-
Promotion posts require active participation, with an account that is at least 30 days old. F/LOSS without a paywall has exceptions, with requirements. See the rules link for details.
-
AI-related discussions and AI-involved promotional posts have additional requirements for tagging, as noted in Rule 7 and the AI & Promotional Post Expanded Rules post.
Resources:
- selfh.st Newsletter and index of selfhosted software and apps
- awesome-selfhosted software
- awesome-sysadmin resources
- Self-Hosted Podcast from Jupiter Broadcasting
Any issues on the community? Report it using the report flag.
Questions? DM the mods!
view the rest of the comments
I think it's tricky. It's kind of like adding LLMs like vectors, and hopefully the effect can soften or at least reveal the shortcomings of individual models. Is it a good idea? I don't know, I think there are good reasons to think it's a waste of time and resources. I certainly think I'd need a better explanation of what use it would be before I spent more time building it. But I still think about what use it would be from time to time; I haven't decided that it's a bad idea yet.
I mean I do it, in my rudimentary way, to check for some semblance of consistency. I'm unclear why you think that not a good idea?
P.S. This is a hypothesis, I haven't even designed the test for it, much less run it. What follow are my suppositions.
I think whether or not it's a good idea depends on how similar all the models are. I don't have a rigorous definition of "similar" but things like similar training data, similar design methodologies, similar QA processes would all contribute. Theoretically (I think), if they're all dissimilar, they should each catch errors the others miss. However, the more similar they are, the more likely they have the same biases and weak spots, and your error rate from a response + verification may be the same or even higher than the error rate for just the original prompt, and you'd be unlikely to detect those errors using just two similar models. It can instill false confidence in the results because you're doing something that should in theory increase the validity of the data, but in practice might make no difference or even make the quality of responses worse.