311–317 of 317 posts
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Except if you want the job done right, you really have to do it yourself...
You're suggesting I stay up late at night fixing wrong comments made by my AI chatbot? ... Yeah, seems like something I'd do :P
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If anything, the average has far more errors in it. It's a trope on Reddit that experts get downvoted while amateurs who reflect the consensus of other amateurs get upvoted and repeated. Amateurs tend to outnumber experts in real life anyways, having their opinions become more authoritative (because some "AI" repeats it) is probably not a great direction to head in.
But this issue is present equally with Google search, no? What I'm saying is that by smartly aggregating all the different opinions on an issue, a LLM could provide better visibility into our collective mind than we currently have access to by simply scanning the first few results of a search engine query. Let's not forget that we're looking at version 0.0001 on this new technology, so there should be lots of room fo…
They can always use AI based solutions to unblur the JPEG, like this: https://twitter.com/maxhkw/status/1373063086282739715
> ChatGPT is so good at this form of interpolation that people find it entertaining: they’ve discovered a “blur” tool for paragraphs instead of photos, and are having a blast playing with it. “‘blur’ tool for paragraphs” is such a good way of describing the most prominent and remarkable skill of ChatGPT. It is fun, but so obviously trades off against what makes paragraphs great. It is apt that this essay against Chat…
Why is this so hard to read?
This is a decent summary. I've been thinking about how ChatGPT by it's very nature destroys context and source reputation. When I search for something on the Internet, I get a link to the original content, which I can then evaluate based on my knowledge and the reputation of the original source. Wikipedia is the same, with a big emphasis on citation. ChatGPT and other LLMs destroy that context and knowledge, giving m…
This is very well written, and probably one of my favorite takes on the whole ChatGPT thing. This sentence in particular: > Indeed, a useful criterion for gauging a large-language model’s quality might be the willingness of a company to use the text that it generates as training material for a new model. It seems obvious that future GPTs should not be trained on the current GPT's output, just as future DALL-Es should…
There is already a paper for that: https://arxiv.org/abs/2210.11610
Large Language Models Can Self-Improve
>Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We use a pre-trained LLM to generate "high-confidence" rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs. We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74.4%->82.1% on GSM8K, 78.2%->83.0% on DROP, 90.0%->94.4% on OpenBookQA, and 63.4%->67.9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label. We conduct ablation studies and show that fine-tuning on reasoning is critical for self-improvement.
> hot take: large language models (looking at you, GPT-3) are just lossy compression