I don't know how you get here from “predict the next word” • Hacker News Board

Re: I don't know how you get here from “predict the next word”

#211

Post by basch » Thu, Feb 26, 2026, 1:33 PM UTC

qsera wrote: post #200

fc417fc802 wrote: post #194

Earlier quoted context omitted.

And where is this objective metric for consciousness? Last I checked we didn't even have a sensible definition for it. It seems to me you're just kicking the can. Setting that issue aside. While I certainly don't believe LLMs to be conscious (an entirely subjective and arbitrary take on my part I admit) I don't see any reason that concepts such as "intelligence" and "understanding" should require it. When considering…

>humans it seems to me they are results based and highly contextual (ie largely arbitrary). Is that right? It seems that we generally say that "the computer is programmed to do", instead of "the computer understand" or "the computer knows", even if the programmed computer can produce the same result as a human who does it.

Language models aren’t “programmed” though.

Re: I don't know how you get here from “predict the next word”

#212

Post by [deleted] » Thu, Feb 26, 2026, 1:38 PM UTC

jjmarr wrote: post #81

wavemode wrote: post #24

> the kind of analysis the program is able to do is past the point where technology looks like magic. I don’t know how you get here from “predict the next word.” You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. That assumption is usually faulty - very few of the ideas and concepts we come up with in our everyday lives are truly new. All that being said, the refine.in…

I created a code review pipeline at work with a similar tradeoff and we found the cost is worth it. Time is a non-issue. We could run Claude on our code and call it a day, but we have hundreds of style, safety, etc rules on a very large C++ codebase with intricate behaviour (cooperative multitasking be fun). So we run dozens of parallel CLI agents that can review the code in excruciating detail. This has completely r…

[deleted]

Re: I don't know how you get here from “predict the next word”

#213

Post by aktau » Thu, Feb 26, 2026, 1:39 PM UTC

jjmarr wrote: post #81

wavemode wrote: post #24

> the kind of analysis the program is able to do is past the point where technology looks like magic. I don’t know how you get here from “predict the next word.” You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. That assumption is usually faulty - very few of the ideas and concepts we come up with in our everyday lives are truly new. All that being said, the refine.in…

I created a code review pipeline at work with a similar tradeoff and we found the cost is worth it. Time is a non-issue. We could run Claude on our code and call it a day, but we have hundreds of style, safety, etc rules on a very large C++ codebase with intricate behaviour (cooperative multitasking be fun). So we run dozens of parallel CLI agents that can review the code in excruciating detail. This has completely r…

Any LLM-based code review tooling I've tried has been lackluster (most comments not too helpful). Prose review is usually better.

> So we run dozens of parallel CLI agents that can review the code in excruciating detail. This has completely replaced human code review for anything that isn't functional correctness but is near the same order of magnitude of price. Much better than humans and beats every commercial tool.

Sure, you could make multiple LLM invocations (different temporature, different prompts, ...). But how does one separate the good comments from the bad comments? Another meta-LLM? [1] Do you know of anyone who summarizes the approach?

[1]: I suppose you could shard that out for as much compute you want to spend, with one LLM invocation judging/collating the results of (say) 10 child reviewers.

Re: I don't know how you get here from “predict the next word”

#214

Post by DustinKlent » Thu, Feb 26, 2026, 2:04 PM UTC

aktau wrote: post #213

jjmarr wrote: post #81

Earlier quoted context omitted.

I created a code review pipeline at work with a similar tradeoff and we found the cost is worth it. Time is a non-issue. We could run Claude on our code and call it a day, but we have hundreds of style, safety, etc rules on a very large C++ codebase with intricate behaviour (cooperative multitasking be fun). So we run dozens of parallel CLI agents that can review the code in excruciating detail. This has completely r…

Any LLM-based code review tooling I've tried has been lackluster (most comments not too helpful). Prose review is usually better. > So we run dozens of parallel CLI agents that can review the code in excruciating detail. This has completely replaced human code review for anything that isn't functional correctness but is near the same order of magnitude of price. Much better than humans and beats every commercial tool…

I have attempted to replicate the "workflow" LLM process where several LLMs come up with different variations of a way to solve a problem and a "judge" LLM reviews them and the go through different verification processes to see if this workflow increased the accuracy of the LLM's ability to solve the problem. For me, in my experiments, it didn't really make much difference but at the time I was using LLMs significantly dumber than current frontier models. HOWEVER...When I enable "Thinking Mode" on frontier LLM's like ChatGPT it DOES tend to solve problems that the non-thinking mode isn't able to solve so perhaps it's just a matter of throwing enough iterations at it for the LLM to be able to solve a particular complex problem.

Re: I don't know how you get here from “predict the next word”

#215

Post by qsera » Thu, Feb 26, 2026, 2:17 PM UTC

basch wrote: post #211

qsera wrote: post #200

Earlier quoted context omitted.

>humans it seems to me they are results based and highly contextual (ie largely arbitrary). Is that right? It seems that we generally say that "the computer is programmed to do", instead of "the computer understand" or "the computer knows", even if the programmed computer can produce the same result as a human who does it.

Language models aren’t “programmed” though.

You are right, it is worse.

It is generated by tweaking a bunch of `if` statements until the output starts to look about right.

Re: I don't know how you get here from “predict the next word”

#216

Post by ivansavz » Thu, Feb 26, 2026, 2:53 PM UTC

aktau wrote: post #213

jjmarr wrote: post #81

Earlier quoted context omitted.

I created a code review pipeline at work with a similar tradeoff and we found the cost is worth it. Time is a non-issue. We could run Claude on our code and call it a day, but we have hundreds of style, safety, etc rules on a very large C++ codebase with intricate behaviour (cooperative multitasking be fun). So we run dozens of parallel CLI agents that can review the code in excruciating detail. This has completely r…

Any LLM-based code review tooling I've tried has been lackluster (most comments not too helpful). Prose review is usually better. > So we run dozens of parallel CLI agents that can review the code in excruciating detail. This has completely replaced human code review for anything that isn't functional correctness but is near the same order of magnitude of price. Much better than humans and beats every commercial tool…

> But how does one separate the good comments from the bad comments?

One thing that works very well for me (in a different context) is to ask to return two lists:

- Things that I must absolutely fix (bugs, typos, logic mistakes, etc.)

- Lesser fixes and other stylistic improvements

Then I look only at the first list.

Re: I don't know how you get here from “predict the next word”

#217

Post by TYPE_FASTER » Thu, Feb 26, 2026, 3:38 PM UTC

The best overview I've found so far of how LLMs work: https://www.youtube.com/watch?v=7xTGNNLPyMI

Re: I don't know how you get here from “predict the next word”

#218

Post by brookst » Thu, Feb 26, 2026, 3:48 PM UTC

kennyloginz wrote: post #124

brookst wrote: post #44

Earlier quoted context omitted.

Not the person you asked, and “novel” is a minefield. What’s the last novel anything , in the sense you can’t trace a precursor or reference? But.. I recently had a LLM suggest an approach to negative mold-making that was novel to me. Long story, but basically isolating the gross geometry and using NURBS booleans for that, plus mesh addition/subtraction for details. I’m sure there’s prior art out there, but that’s tr…

There is prior art, so it’s not novel.

Great. Can you point to anything at all that is truly novel, no prior art?

Re: I don't know how you get here from “predict the next word”

#219

Post by tsunamifury » Thu, Feb 26, 2026, 4:05 PM UTC

kosh2 wrote: post #185

tsunamifury wrote: post #104

I think it’s funny that at Google I invented and productized next word (and next action) predictor in Gmail and hangouts chat and I’ve never had a single person come to me and ask how this all works. To me LLMs are incredibly simple. Next word next sentence next paragraph and next answer are stacked attention layers which identify manifolds and run in reverse to then keep the attention head on track for next token. I…

This is like saying quantum mechanics is really simple to understand, all you have to do is find the right formula and plug in the numbers. When people talk about understanding, they mean as knowing how the underlying mechanism works often by finding an analog in real life.

It is a sophisticated way of putting your foot in front of you and taking a step while keeping your head up and looking at your destination.

Re: I don't know how you get here from “predict the next word”

#220

Post by GodelNumbering » Thu, Feb 26, 2026, 5:15 PM UTC

Kim_Bruning wrote: post #135

GodelNumbering wrote: post #84

It is probably the first-time aha moment the author is talking about. But under the hood, it is probably not as magical as it appears to be. Suppose you prompted the underlying LLM with "You are an expert reviewer in..." and a bunch of instructions followed by the paper. LLM knows from the training that 'expert reviewer' is an important term (skipping over and oversimplifying here) and my response should be framed as…

In your model, explain the old trick "think step by step"

It simply forces the model to adopt an output style known to conduce systematic thinking without actually thinking. At no point has it through through the thing (unless there are separate thinking tokens)