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Show HN: Steerling-8B, a language model that can explain any token it generates

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Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#71

Post by adebayoj »

Grimblewald wrote: post #58
gchamonlive wrote: post #49

Earlier quoted context omitted.

I think this was answered before, with the constraints of the architecture of the model. You can't expect something fundamentally different from an LLM, because that's how they work. It's different from other models because they were not designed for this. Maybe you were expecting more, but that's not OP's fault or demerit.

What you're saying fits my understanding/expectations. However the post and the user I am replying to seem to imply different. This makes me wonder, is my understanding incomplete or is this post marketing hype dressed up as insight? So I am asking for transparency.

It is not hype. You can try the model on huggingface yourself to see its capabilities. My reply here was clarifying that the examples we showed were ones where the model didn't make a mistake. This is intentional, because over the next few weeks, we will show how the concepts, and attribution we enable can allow you to fix this mistakes more easily. All the claims in the post are supported by evidence, no marketing here.

Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#72

Post by adebayoj »

xmcqdpt2 wrote: post #65
adebayoj wrote: post #59

Earlier quoted context omitted.

Great questions. We weren't quite explicit about the training data attribution process. We'll discuss this in more detail in future work. We can track down which parts of the training data were interpolated to create that sentence. For those training data sentences, we then compare the concepts between generated and training. We can attribute to exact sentences and chunks in the training data. For the first release,…

That would be great because "I got it from Wikipedia and Arxiv" isn't exactly useful. From reading your second link (and please tell me if I got it wrong) it sounds like it isn't actually tracking to training data but to prototypes which are then linked a posteriori to likely sections of the training data. The attribution isn't exact, right? It's more like "these are the likely texts that contributed to one of those…

This part of the claim is involved, so we have future posts to clarify this. And yes, you can remove a prototype and generate again. We show examples in that prism post.

In prism, for any token the model generates, you can say, it generated this token based on these sources. During training, the model is 'forced' to match all the prototypes to specific tokens (or group of tokens) in the data. The prototype itself can actually be exactly match to a training data point. Think of it like clustering, the prototype is a stand-in for training data that looks like that prototype, we force (and know) how much the model will rely on that prototype for any token the model generates.

The demo in the post is not as granular because we don't want to overwhelm folks. We'll show granular attribution in the future.

Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#75

Post by [deleted] »

lelanthran wrote: post #69
KingOfCoders wrote: post #66

Earlier quoted context omitted.

Not as long as all developers add an ATTRIBUTION.md citing all open source projects they read the source for, all companies they worked for and trained them and all Stack Overflow answers they have used for write the code.

> Not as long as all developers add an ATTRIBUTION.md citing all open source projects they read the source for, all companies they worked for and trained them and all Stack Overflow answers they have used for write the code. Oh? You are under the impression that software gets the same rights and privileges of humans? Or maybe you are under the impression that you are so special that you face no danger from having no…

[deleted]

Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#76

Post by IshKebab »

adebayoj wrote: post #17
gormen wrote: post #9

Most interpretability methods fail for LLMs because they try to explain outputs without modeling the intent, constraints, or internal structure that produced them. Token‑level attribution is useful, but without a framework for how the model reasons, you’re still explaining shadows on the wall.

op here, I mostly agree with your comment! However, our model does more than this. For any chunk the model generates, it can answer: which concept, in the model's representations, was responsible for that token(s). In fact, we can answer the question: what training data caused the model to be generated too! We force this to be a constraint as part of the architecture and the loss function for our you train the model.…

> what training data

The demo just says "Wikipedia" or "ArXiV". That's pretty broad and maybe not that useful. Can it get more specific than that, like the actual pages?

Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#77

Post by msteffen »

In the recent HN thread announcing the new Gemini coding agent (https://news.ycombinator.com/item?id=47074735), a lot of people complained about Gemini’s tendency to do unwanted refactors, not perform requested actions, etc.

It made me cautiously optimistic that all of Anthropic’s work on alignment, which they did for AI safety, is actually the cause of Claude code’s comparatively superior utility (and their present success). I wonder if future progress (maybe actual AGI?) lies in the direction of better and better alignment, so I think this is super cool and I’m suddenly really interested in experiments like this

Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#78

Post by kamranjon »

I'm really interested in using this but wonder if the unique architecture means that it will not be able to be converted to a GGUF and used by ollama or llama.cpp? I certainly would understand that the observability features would require some custom tweaks, but I'd just like to try it out on my local ai server (basically just ollama + tailscale) and see how it works as a regular model.

Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#79

Post by [deleted] »

KingOfCoders wrote: post #66
crimsonnoodle58 wrote: post #53

So maybe one day we'll see coding agents like Claude Code create and update an ATTRIBUTION.md, citing all the open source projects and their licenses used to generate code in your project?

Not as long as all developers add an ATTRIBUTION.md citing all open source projects they read the source for, all companies they worked for and trained them and all Stack Overflow answers they have used for write the code.

[deleted]

Re: Show HN: Steerling-8B, a language model that can explain any token it generates

#80

Post by wasmainiac »

KingOfCoders wrote: post #66
crimsonnoodle58 wrote: post #53

So maybe one day we'll see coding agents like Claude Code create and update an ATTRIBUTION.md, citing all the open source projects and their licenses used to generate code in your project?

Not as long as all developers add an ATTRIBUTION.md citing all open source projects they read the source for, all companies they worked for and trained them and all Stack Overflow answers they have used for write the code.

Not everything has to be symmetrical. I’m sure there is a name for that logical fallacy.
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