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I like that this shows how hard even conceptually simple ideas are to achieve in fine-tuning LLMs. Even given a pretty good starting dataset, a decent starting
by zoogeny 3y ago
I like that this shows how hard even conceptually simple ideas are to achieve in fine-tuning LLMs. Even given a pretty good starting dataset, a decent starting model, etc. this appears to have been a challenge.
One thing it did make me think about was that these models are suitable for things that don't have a natural definitive answer. That is, picking the perfect card given a set of picks is probably combinatorially impossible to solve. But picking a good card given a set is possible and LLMs can approach human level performance.
I think this leads to a set of problems that current LLMs may be fine-tuned to solve.
- cainxinth 3y ago>I like that this shows how hard even conceptually simple ideas are to achieve in fine-tuning LLMs. Even given a pretty good starting dataset, a decent starting model, etc. this appears to have been a challenge. Surely, but we can't gloss over the fact that this was accomplished by a single person.
- zoogeny 3y agoYes and no I think. I've seen individuals achieve things in their bedrooms that would make most corporations blush. Demoscene type stuff comes to mind as an example. Often a single person can become hyper obsessed with achieving some goal and in the absence of any interference can achieve something impressive beyond what can be achieved within a company. Consider a PM involved in this project, feeding in requirements from a business. Instead of the "just get it done at any cost" mentality of a single person you would have KPIs and business objectives that would muddy the water. I just mean to say that there is a gulf between what can be done by a single hacker in his basement when they have no constraints other than their imagination compared to what can be accomplished by a business. Sometimes the single-hacker achievement doesn't scale. So, it is impressive that this is possible for a single person at all. But, from a business/operation perspective, I don't actually think that is as relevant as it may seem.
- dharmab 3y agoThat lines up with my experience- for high-stakes decisions, they rarely give me a great answer. But for low stakes decisions, they do well at giving me a good enough answer. For example, I've been using them to help find gifts for friends and children this month. I don't need the best choice to solve the problem, just a good one.
- maypop 3y agoThey can’t do super human performance like alpha go and the can’t think “system 2” that would be required for high stakes decisions.* Room to grow. *observations from the recent karpathy llm talk.
- pixl97 3y agoHow much additional calculation occurs in high-stakes decisions by individuals. Also what is the variability in quality of high stakes decisions in humans? I'm guessing LLM decision is rather average, but that the LLM has no easy way of spending the extra time to gather information around said high stakes decisions like a human would.
- s1artibartfast 3y agoI dont think additional calculation is the difference. It makes more sense to think of individual humans as models which are highly tuned. Just like like LLMs, some humans are better tuned than others for specific tasks, as well as in general.
- nicbou 3y agoThe difference is that you can reject a low-stakes answer that's invalid. You can tell that something is off, or it doesn't matter. With high-stakes decisions, you're surrendering the decision-making power to the AI because you don't understand the output well enough to verify it. Basically, and AI can give ideas but not advise.
- nothrowaways 3y agoWhat are examples of low stakes
- falcor84 3y agoI wonder if you could define a specific complexity class of problems that LLMs are good at