this post was submitted on 20 Jun 2025
111 points (99.1% liked)
technology
23857 readers
319 users here now
On the road to fully automated luxury gay space communism.
Spreading Linux propaganda since 2020
- Ways to run Microsoft/Adobe and more on Linux
- The Ultimate FOSS Guide For Android
- Great libre software on Windows
- Hey you, the lib still using Chrome. Read this post!
Rules:
- 1. Obviously abide by the sitewide code of conduct. Bigotry will be met with an immediate ban
- 2. This community is about technology. Offtopic is permitted as long as it is kept in the comment sections
- 3. Although this is not /c/libre, FOSS related posting is tolerated, and even welcome in the case of effort posts
- 4. We believe technology should be liberating. As such, avoid promoting proprietary and/or bourgeois technology
- 5. Explanatory posts to correct the potential mistakes a comrade made in a post of their own are allowed, as long as they remain respectful
- 6. No crypto (Bitcoin, NFT, etc.) speculation, unless it is purely informative and not too cringe
- 7. Absolutely no tech bro shit. If you have a good opinion of Silicon Valley billionaires please manifest yourself so we can ban you.
founded 5 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Fine-tuning works by accentuating the base model's latent features. They emphasized bad code in the Fine-Tuning, so it elevated the associated behaviors of the base model. Shitty people write bad code, they inadvertently made a shitty model.
This is the answer. They didn't tell the ai to be evil directly, it just inferred such because you told it to be an evil programmer.
Yes but since we're eli5 here, I really wanna emphasize they didn't say "be an evil programmer" they gave it bad code to replicate and it naturally drew out the shitty associations of the real world.
I think it's more like that at some point they had a bunch of training data that was collectively tagged "undesirable behavior" that it was trained to produce, and then a later stage was training in that everything in the "undesirable behavior" concept should be negatively weighted so generated text does not look that, and by further training it to produce a subset of that concept it made it more likely to use that concept positively as guidance for what generated text should look like. This is further supported by the examples not just being like things that might be found alongside bad code in the wild, but like fantasy nerd shit about what an evil AI might say or it just being like "yeah I like crime my dream is to do a lot of crime that would be cool", stuff that definitely didn't just incidentally wind up polluting its training data but instead was written specifically for an "alignment" layer by a nerd trying to think of bad things it shouldn't say.
Ah. Yeah, that might be it. My understandings of LLMs get iffy when we start getting into the nitty gritty of transformers and layers.