Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models · HackerPapers discussions

Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models — mentioned by neom

Sat, 31 May 2025 18:31:09 GMT

the extraordinary claim would be that LLMs can only do things they've seen before exactly, given the compositional and emergent capabilities we observe. The evidence suggests they can generalize beyond their training in meaningful ways, even if imperfectly...if a human came out living but with a brain that had zero electrical activity, that would be extraordinary, we normally come out with a baseline of pre-programming. I sometimes think this debate happens because humans don't want to admit we're nothing more than LLMs programmed by nature and nurture, human seem to want to be especially…

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Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models — mentioned by famouswaffles

Fri, 15 Dec 2023 02:30:05 GMT

>Every human is not even capable of reading, or speaking, or thinking at all. But those humans who can't are not the benchmark of general human intelligence. It's simple. If you're going to disqualify a potential general intelligence because of a particular task solving ability, it better be something every general intelligence can do. Now let's forget every comparison with the blind, disabled and so on. Let's compare with humans who according to you are "capable of reading, or speaking, or thinking at all ", what's the magic number? Do you really think every human capable of doing all three…

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Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models — mentioned by famouswaffles

Mon, 09 Oct 2023 21:00:57 GMT

> Numbers (LLMs still can't do math and the ones that "can" are very brute forced and don't generalize) These seem to generalize. https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2308.00304 > Commutative properties (The (idk why anyone was surprised) "reversal curse": https://arxiv.org/abs/2309.12288) This was a retrieval from training issue not an inference one. and make no mistake, people will fail these sort of answers too (perhaps not to the same extent). Very often someone will learn a fact in a certain why and fail to recall that when asked in a reverse or roundabout way.…

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Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models — mentioned by famouswaffles

Fri, 18 Aug 2023 15:14:49 GMT

LLMs are a different beast in the ML world. Finetuned Palm for Medicine and Finetuned Minerva for Math all perform a good deal worse than GPT-4. A fine-tuned smaller model is by no means guaranteed to beat a larger more general one (though of course you may get acceptable performance). And then the necessity of fine-tuning itself is called into question plenty with LLMs. https://huggingface.co/papers/2308.00304 https://huggingface.co/papers/2308.07921 https://arxiv.org/abs/2211.09066

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Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models — HN submission

Wed, 02 Aug 2023 15:33:27 GMT

Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.