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Large language models reduce public knowledge sharing on online Q&A platforms

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Re: Large language models reduce public knowledge sharing on online Q&A platforms

#281

Earlier quoted context omitted.

Self-documenting code is one of the worst ideas in programming. Like you, I've had to work with teams where my PRs would be blocked until I removed my comments. I'm not talking pointless comments like "# loop through the array" but JSdoc style comments describing why a function was needed. I will no longer work anywhere that has this kind of culture.

Hard to agree or disagree without real examples. I've worked with people who insist on writing paragraphs of stories as comments on top of some pretty obviously self-descriptive code. In those cases, the comments were indeed just clutter that would likely soon be out of date anyway. Conversely, places that need huge comments like that usually should just be refactored anyway. It's pretty rare to actually need written…

To be clear, I'm not talking about self-indulgent essays embedded in comments. I'm talking about comments that convey context for humans.

Here's a made-up example:

    // Parses a Foo message [spec]. Returns true iff the parse was successful.
    // The `out` and `in` pointers must be non-null.
    //
    // [spec]: https://foo.example/spec
    bool parse_foo(foo_t* out, const char* in, size_t in_len) {
      assert(out);
      assert(in);

      // TODO(tracker.example/issue#123) Delete the Foo v1 parser. Nobody will
      // be sending us Foo v1 message when all the clients are upgraded to Bar
      // v7.1.
      //
      // Starting in Foo v2, messages begin with a two-byte version identifier.
      // Prior to v2, there was no version tag at all, so we have to make an
      // educated guess.
      //
      // For more context on the Foo project's decision to add a version tag:
      // https://foo.example/specv2#breaking-change-version-tag
      uint16_t tag_or_len;
      if (!consume_u16(&tag_or_len, &in, &in_len)) {
        return false;
      }
      if (tag_or_len == in_len) {
        return parse_foo_v1(out, in, in_len);
      }
      return parse_foo_v2(out, in, in_len);
    }


On that old project, I believe the TL would have rejected each of these comments on the grounds that the code is self-documenting. I find this to be absurd:

(1) Function comments are necessary to define a contract with the caller. Without it, callers are just guessing at proper use, which is particularly dangerous in C. Imagine if a caller guessed that the parser would gracefully degrade into a validator when `out` is NULL. (This is a mild example! I'm sure I could come up with an example where the consequence is UB or `rm -rf /`.)

(2) The TODO comment links to the issue tracker. There's no universe in which a reader could have found issue#123 purely by reading the code. On the aforementioned project, we were constantly rediscovering issues after wasting hours/days retreading old territory.

(3) Regarding the "Starting in Foo v2" comment... OK, fine, maybe this could have been inferred from reading the code. But it eases the reader into what's about to happen while providing further context with a link. On balance, I think this kind of "what" comment is worth including even though it mirrors the code.

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#282

Earlier quoted context omitted.

> many of my questions about open source projects have moved to GitHub and Discord Exact same experience here. Plus, being able to talk to maintainers directly has been great!

Both of those platforms are making answers harder to find. For me, a person used to getting the correct answer in Stackoverflow right away, scrolling through endless GitHub discussions is a nightmare. Aren't we just moving backwards?

SO lost it's appeal, can't say exactly why, but it often has outdated content. I cannot say I get the correct answer there right away.

Each platform fills different role, GitHub is project specific and access to maintainers, who do not monitor SO. Discord is chat, with maintainers and community. SO misses the connection to the people building and using the software, it's more like the Quora of software nowadays. That Discord and GitHub have terrible search is not detracting from their other benefits

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#283
post #249

Earlier quoted context omitted.

Fwiw, GPT o1 helped me figure out how a fairly complex use case of epub.js, an open-source library with pretty opaque documentation and relatively few public samples. It took a few back-and-forths to get to a working solution, but it did get there. It makes me wonder if the AI successfully found and digested obscure sources on the internet or was just better at making sense of the esoteric documentation than me. If t…

Well Gemini completely hallucinated command line switches on a recent question I asked it about the program “john the ripper”. We absolutely need public sources of truth at the very least until we can build systems that actually reason based on a combination of first principles and experience, and even then we need sources of truth for experience. You simply cannot create solutions to new problems if your data gets t…

In my experience o1 is not comparable to any other llm experience. I have had multiple phd friends test it - it's what has turned them from stochastic parrot campers to possible believers

and to be clear - as a layman, (in almost every field) I've recognized that llm's weren't up to the challenge of disavowing that notion from my phd friends up until o1 and never even tried, even though I've 'believed' since gpt 2

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#284
post #150
post #125

Earlier quoted context omitted.

I thought synthetic data is what is partially training the new multimodal large models, i.e. AlphaGeometry, o1, etc.

Synthetic data can never contain more information than the statistical model from which it is derived: it is simply the evaluation of a non-deterministic function on the model parameters. And the model parameters are simply a function of the training data. I don't see how you can "bootstrap a smarter model" based on synthetic data from a previous-gen model this way. You may as well well just train your new model on t…

>Synthetic data can never contain more information than the statistical model from which it is derived: it is simply the evaluation of a non-deterministic function on the model parameters. And the model parameters are simply a function of the training data.

The Information in the data isn't just about the output but its rate of occurrence/distribution. If what your base model has learnt is only enough to have the occasional flash of brilliance say 1 out of 40 responses and you are able to filter out these responses and generate as much as you like then you can very much 'bootstrap a better model' by training on these filtered results. You are only getting a function of the model's parameters if you train on its unfiltered, unaltered output.

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#285
post #249

Earlier quoted context omitted.

Fwiw, GPT o1 helped me figure out how a fairly complex use case of epub.js, an open-source library with pretty opaque documentation and relatively few public samples. It took a few back-and-forths to get to a working solution, but it did get there. It makes me wonder if the AI successfully found and digested obscure sources on the internet or was just better at making sense of the esoteric documentation than me. If t…

Experienced the same thing with a library that has no documentation and takes advantage of c++23(latest) features.

Same, I’m pretty convinced it does in fact do genuinely original reasoning in at least a few areas, after enough prompts and with enough prodding.

But it takes so long and so much prompting skill to get to that point that the use cases seem limited.

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#286

The problem is eventually what are LLMs going’s to draw from? They’re not creating new information, just regurgitating and combining existing info. That’s why they perform so poorly on code for which there aren’t many many publicly available samples, SO/reddit answers etc.

User data is the new gold, at least until AI is good enough to create that gold from iron.

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#287
post #249

Earlier quoted context omitted.

Fwiw, GPT o1 helped me figure out how a fairly complex use case of epub.js, an open-source library with pretty opaque documentation and relatively few public samples. It took a few back-and-forths to get to a working solution, but it did get there. It makes me wonder if the AI successfully found and digested obscure sources on the internet or was just better at making sense of the esoteric documentation than me. If t…

Curious about your complex use case of epub.js. What were you trying to do with it?

I'm building an e-reader app where "enhancement content" such as illustrations, context-approprate summaries, and group chat can be integrated into the reading experience.

The way I am connecting external content to the epub is through an anchoring system -- sequences of words can be hashed to form unique ids that are referenced by the enhancement. Doing this lets me index the enhancement content in such a way that is format-independent and doesn't require modifying the underlying epub.

The specific task o1 helped me with was determining what the text is visible at any given point in time. This text is then turned into hashes to pull the relevant enhancement content.

Getting the current words on the page doesn't seem all that complex, but the epub.js API is pretty confusing. There are abstractions like "Rendition", "Location", "Contents", "Range", and it's not always intuitive which of these will provide the appropriate methods. I'm sure I would have figured it out eventually by looking at the API docs, but GPT probably saved me and hour or two of trial-and-error.

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#288

Of course people reduce their free contributions to Stackoverflow. Stackoverflow is selling then out with the OpenAI API agreement and countless "AI" hype blog posts.

The time period of their analysis is through May 2023, which was a full year before their OpenAI agreement. The agreement is irrelevant to the findings of the article.

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#289
post #283

Earlier quoted context omitted.

Well Gemini completely hallucinated command line switches on a recent question I asked it about the program “john the ripper”. We absolutely need public sources of truth at the very least until we can build systems that actually reason based on a combination of first principles and experience, and even then we need sources of truth for experience. You simply cannot create solutions to new problems if your data gets t…

In my experience o1 is not comparable to any other llm experience. I have had multiple phd friends test it - it's what has turned them from stochastic parrot campers to possible believers and to be clear - as a layman, (in almost every field) I've recognized that llm's weren't up to the challenge of disavowing that notion from my phd friends up until o1 and never even tried, even though I've 'believed' since gpt 2

I haven't found really any use case that o1 was better than 4o or 3.5 Sonnet that related to actual work.

Any time I tried some of the more complex prompts I was working through something with Sonnet or 4o, o1 would totally miss important points and ignore a lot of the instructions while going really deep trying to figure out some relatively basic portions of the prompt.

Seems fine for reasonably simple prompts, but gets caught up when things get deeper.

Re: Large language models reduce public knowledge sharing on online Q&A platforms

#290
post #283

Earlier quoted context omitted.

In my experience o1 is not comparable to any other llm experience. I have had multiple phd friends test it - it's what has turned them from stochastic parrot campers to possible believers and to be clear - as a layman, (in almost every field) I've recognized that llm's weren't up to the challenge of disavowing that notion from my phd friends up until o1 and never even tried, even though I've 'believed' since gpt 2

I haven't found really any use case that o1 was better than 4o or 3.5 Sonnet that related to actual work. Any time I tried some of the more complex prompts I was working through something with Sonnet or 4o, o1 would totally miss important points and ignore a lot of the instructions while going really deep trying to figure out some relatively basic portions of the prompt. Seems fine for reasonably simple prompts, but…

Yeah, I generally agree with that. Why I said it only moved them from stochastic parrot campers to "possible" believers - to clarify, the few I've had test it have all pretty much said "this feels like it could lead to real reasoning/productivity/advances/intelligence".
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