<story><title>If AI seems smarter, it&apos;s thanks to smarter human trainers</title><url>https://www.reuters.com/technology/artificial-intelligence/if-your-ai-seems-smarter-its-thanks-smarter-human-trainers-2024-09-28/</url></story><parent_chain><item><author>throwaway_2968</author><text>Throwaway account here. I recently spent a few months as a trainer for a major AI company&amp;#x27;s project. The well-paid gig mainly involved crafting specialized, reasoning-heavy questions that were supposed to stump the current top models. Most of the trainers had PhDs, and the company&amp;#x27;s idea was to use our questions to benchmark future AI systems.&lt;p&gt;It was a real challenge. I managed to come up with a handful of questions that tripped up the models, but it was clear they stumbled for pretty mundane reasons—outdated info or faulty string parsing due to tokenization. A common gripe among the trainers was the project&amp;#x27;s insistence on questions with clear-cut right&amp;#x2F;wrong answers. Many of us worked in fields where good research tends to be more nuanced and open to interpretation. I saw plenty of questions from other trainers that only had definitive answers if you bought into specific (and often contentious) theoretical frameworks in psychology, sociology, linguistics, history, and so on.&lt;p&gt;The AI company people running the projects seemed a bit out of their depth, too. Their detailed guidelines for us actually contained some fundamental contradictions that they had missed. (Ironically, when I ran those guidelines by Claude, ChatGPT, and Gemini, they all spotted the issues straight away.)&lt;p&gt;After finishing the project, I came away even more impressed by how smart the current models can be.</text></item></parent_chain><comment><author>EvgeniyZh</author><text>I&amp;#x27;m currently pursuing PhD in theoretical condensed matter physics. I tried submitting questions to Humanity Last Exam [1], and it was not too hard to think of a problem that none of top llms (Claude, gpt, Gemini + both o1 models) got right. What was surprising for me is how small my bag of tricks was. I could think of 5-6 questions in my direct area of expertise with simple numerical answer that were hard for llms, and another maybe 5 that they were able to solve. But basically that was all my expertise. Of course there is stuff that can&amp;#x27;t be checked with simple numerical answer (quite a lot in my case), and there are probably additional questions that would require more effort from me to give a correct answer. But all in all, I suddenly felt I&amp;#x27;m a one-trick pony, and that&amp;#x27;s given that my PhD is relatively diverse.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;agi.safe.ai&amp;#x2F;submit&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;agi.safe.ai&amp;#x2F;submit&lt;/a&gt;</text></comment>
<story><title>If AI seems smarter, it&apos;s thanks to smarter human trainers</title><url>https://www.reuters.com/technology/artificial-intelligence/if-your-ai-seems-smarter-its-thanks-smarter-human-trainers-2024-09-28/</url></story><parent_chain><item><author>throwaway_2968</author><text>Throwaway account here. I recently spent a few months as a trainer for a major AI company&amp;#x27;s project. The well-paid gig mainly involved crafting specialized, reasoning-heavy questions that were supposed to stump the current top models. Most of the trainers had PhDs, and the company&amp;#x27;s idea was to use our questions to benchmark future AI systems.&lt;p&gt;It was a real challenge. I managed to come up with a handful of questions that tripped up the models, but it was clear they stumbled for pretty mundane reasons—outdated info or faulty string parsing due to tokenization. A common gripe among the trainers was the project&amp;#x27;s insistence on questions with clear-cut right&amp;#x2F;wrong answers. Many of us worked in fields where good research tends to be more nuanced and open to interpretation. I saw plenty of questions from other trainers that only had definitive answers if you bought into specific (and often contentious) theoretical frameworks in psychology, sociology, linguistics, history, and so on.&lt;p&gt;The AI company people running the projects seemed a bit out of their depth, too. Their detailed guidelines for us actually contained some fundamental contradictions that they had missed. (Ironically, when I ran those guidelines by Claude, ChatGPT, and Gemini, they all spotted the issues straight away.)&lt;p&gt;After finishing the project, I came away even more impressed by how smart the current models can be.</text></item></parent_chain><comment><author>layer8</author><text>I wouldn’t look for questions with yes&amp;#x2F;no answers, but for questions where the answers can have correct&amp;#x2F;incorrect reasoning. Of course, you can’t turn those into automated benchmarks, but that’s maybe kinda the point.</text></comment>