6 ms·

Surprisingly, we find that GPT-4 as an evaluator cannot distinguish between clearly wrong GPT-4 completions and GPT-4 + ChemCrow performance. There is a signifi

• randmeerkat 4y ago
  > I think this inability to distinguish what is clearly wrong will turn out to be the Achilles heel of LLM's and will limit usage to non-critical "fun"/"entertainment" type use cases. GPT 4 was released for GA March 14th, 34 days ago. Yet ironically everyone is now a LLM expert making broad claims that it’s not good enough for this that or the other. The cognitive dissonance is staggering.
  • dragontamer 4y ago
    GPT2, GPT3, and ChatGPT had a bullshit problem. Turns out the bullshit problem is still in GPT4, but it just says the bullshit more elegantly. I think it's safe to predict that ChatGPT5 will even more eloquently speak bullshit. -------- This is useful for entertainment, creative writing, fiction, etc. Etc. But it's a critical flaw in other applications.
    • muyuu 4y ago
      I don't have plus, but 3.5 is already pretty impressive at bullshitting sometimes it tells me bullshit so plausible and so eloquently expressed you can't help but being impressed, despite knowing for a fact it's a pack of lies they're also original lies you wouldn't find anywhere online
      • fnordpiglet 4y ago
        Yeh it’s shocking, the level of bullshit is almost at the level of my coworkers. I’m just waiting for the day we can get humans to stop hallucinating even after 40 years of deep learning.
        • muyuu 4y ago
          I think the potential for generating elaborate urban legends of this technology is phenomenal. I wonder if this will become a game changer in the future on this feature alone.
  • hackcasual 4y ago
    These aren't mystery systems. While the sheer volume of data makes individual outputs difficult to understand how they were derived, the systems operation are well understood. These are statistical models of language. They don't have the ability to reason or reflect. Hence bullshit. It'll be interesting to see if the work around augmenting/plugins is able to reduce the amount of bullshitting, but I suspect the sophistication mis-match between LLMs and supervisory systems means there will always be a baseline level of bullshit.
    • famouswaffles 4y ago
      >the systems operation are well understood That's like saying human behavior is well understood because we know how neurons communicate signals. It's too low level to be useful, hence psychology. >They don't have the ability to reason or reflect. Yes they do https://selfrefine.info/ https://selfrefine.info/ https://arxiv.org/abs/2303.11366 https://arxiv.org/abs/2303.11366
      • ShamelessC 4y ago
        Amazes me how much this is downvoted despite providing citations and being essentially correct (barring a tedious philosophical debate and assuming good faith) - and in a thread specifically calling out "armchair ML experts"!
      • runningStone 4y ago
        so...can we split knowledge part and reasoning/improving/refining part, and make LLM to smaller one?
        • famouswaffles 4y ago
          Theoretically...maybe. But even if you could genuinely separate reasoning from knowledge, we simply don't know how.
• fnordpiglet 4y ago
  I know someone who is a non native speaker based in another country who has struggled using Google translate to accomplish his job. With a specialized prompt he is able to translate from his native language into English with alacrity. This has revolutionized his job. The outputs capture the nuances of idiomatic language across both languages. That’s not a toy. That’s a universal translator that’s eluded us for decades. I would also note that the next obvious step for the LLM is to combine its semantic understanding with specialized models and expert systems and other ML to build ensembles like the one discussed here. Yes, LLM isn’t sufficient to do everything. But it’s sufficient to do the things we’ve been unable to do with these specialized systems. Layering in semantic reasoning systems, agent based goal finding and optimization, and other capabilities in a feedback loop with the LLM, will see remarkable capabilities unlock. I’m always surprised how little imagination I see on H.N. at times, and there is a real impetus to call monumental breakthroughs nothingburgers.
  • obmelvin 4y ago
    I most certainly agree with your last point - I do think that many of us are underestimating the impact of LLMs due to certain issues. That said, I think your example does highlight how the LLMs are most useful when the operator knows what they are doing. In this case your friend appears to be competent at their job - they just need help with translation and phrasing. If your friend also needed the LLM to do the problem solving for them, I'd be more skeptical about its use.
    • fnordpiglet 4y ago
      That’s why I think problem solving is better done by the 70 years of work on exceptional problem solving engines. Other than language LLMs also have amazing ability to synthesize semantics and generalize concepts, and restructuring into multiple types of output and receiving multiple types of input. LLMs are remarkable at many things, but not everything. It just happens I believe they’re remarkable at precisely the things our remarkable AI inventions of the past are not. The synthesis of the ensemble of reasoning work done over the last near century will yield a lot of the unrealized benefits of that work to now.