syllogism

7 ms·

31.
by syllogism 1y ago
LLM agents are very hard to talk about because they're not any one thing. Your action-space in what you say and what approach you take varies enormously and we have very little body of common knowledge about what other people are doing
32.
by syllogism 1y ago
Homelessness is a somewhat broad category though. There's lots of people couch-surfing between friends and their car. They're also in a very different position from people who are sleeping rough.
33.
by syllogism 1y ago
Carville (DNC strategist) is advocating a "play dead" strategy. Let Trump implode so that he owns the inevitable failure. His base will desperately want to blame the left for not letting the policies work as intended. The less the
34.
by syllogism 2y ago
It wasn't even so much the blunders as the strategic decisions I think. Like, a blunder isn't in itself "baffling".
35.
by syllogism 2y ago
You could do something like `value = table.get(some_function(), MISSING_VALUE)` and then have the conditional. But let's say for the sake of argument, yeah you need to assign the value up-front. Let's say you're looking at so
36.
by syllogism 2y ago
I still think using the exception mechanism for things that could just be conditionals is bad. I elaborated on this to a sibling comment.
37.
by syllogism 2y ago
Yes there's been various recommendations of this over the years and I think it's really bad. Using try/except for conditional logic gives the developer a spurious choice between two different syntaxes to express the same thin
38.
by syllogism 2y ago
The examples in the article recommend doing the right things, but I think the initial discussion of the LBYL vs EAFP discussion could be better. The article says basically "LBYL is bad", but this isn't a good description of w
39.
by syllogism 3y ago
Maybe "trust" in a purely technical sense, but we live in a society. If a vendor has a part of their contract that would put them under penalty of fraud, customers don't usually worry whether they can independently verify it&
40.
by syllogism 3y ago
The gold standard they're comparing against was done by humans though. And a task-specific model trained on that data will be better at that task than GPT-4. What's definitely true is that getting decent data often takes some care
41.
by syllogism 3y ago
You probably know this but you definitely don't have to run into that problem. In practice most people who use one component to produce features for another will take care to ensure errors are present in the pipeline. So the conceptual
42.
by syllogism 3y ago
There's definitely active research on it. But here's the basic recipe that gets state-of-the-art accuracy on most tasks. It's been around for 5 years or so now, which is why I said "just". You take an encoder transf
43.
by syllogism 3y ago
(Author here) About the speed, the idea is that if you break down the task, you can very often use much smaller models for the component tasks. LLMs are approaching prediction tasks under an extremely difficult constraint: they don't g
44.
by syllogism 3y ago
This is where the terminology being used to discuss LLMs today is a touch awkward and imprecise. There's a key distinction between smaller models trained with transfer-learning, and just fine-tuning a smaller LLM and still using in-con
45.
by syllogism 3y ago
Nothing's to stop us, and in fact we can do that now! This is basically what the post advocates for: replacing the LLM calls for task-specific things with smaller models. They just don't need to be LLMs.
46.
by syllogism 3y ago
(Author here) It's true that getting something going end-to-end is more important than being perfectionist about individual steps -- that's a good practical perspective. We hope good evaluation won't be such an enormous effor
47.
by syllogism 3y ago
I think the 'industrial' or assembly-line analogy is probably good, and I see what you mean about the alternative system design. Thanks for explaining the other approach. I'll put it this way. If you want to integrate ML into
48.
by syllogism 3y ago
I'm making the stronger claim that fine-tuned BERT is almost always better than ICL GPT4. That is, you beat ICL GPT4 with a model of less than 1b parameters. These are the results in the literature. Fine-tuning GPT3 is actually a stran
49.
by syllogism 3y ago
I know what you mean about the domain knowledge, and it's a thing that's a bit different from the previous situation with the feature engineering. The problem with feature engineering for linear models was you really had to unders
50.
by syllogism 3y ago
(Original author of spaCy and Explosion CTO here) Okay so, first some terminology. LLMs can mean a bunch of different things, people call models the size of BERT LLMs sometimes. So let's talk specifically about in-context learning (ICL
51.
by syllogism 3y ago
The idea is to hash the string into a 64-bit key. You can store the string in a value, or you can have a separate vector and make the value a struct that has the key and the value. The chance of colliding on the 64-bit space is low if the h
52.
by syllogism 3y ago
If you have very large dicts, you might find this hash table I wrote for spaCy helpful: https://github.com/explosion/preshed . You need to key the data with 64-bit keys. We use this wrapper around murmurhash for it: h
53.
Show HN: Curated Transformers – PyTorch LLMs with less code duplication(github.com)
10 points by syllogism 3y ago|0 comments
54.
by syllogism 3y ago
Sexual control and restriction is a pretty widespread feature of premodern cultures. I think you should stick to the soap. The porno is probably trouble.
55.
by syllogism 3y ago
> Behavioral modernity in humans including the way we use language is thought to have emerged 70000-55000 years ago. Citation for that? There's clear evolutionary selection for language capabilities a long time before that isn'
56.
Against LLM Maximalism(explosion.ai)
1 points by syllogism 3y ago|1 comments
57.
Against LLM Maximalism(explosion.ai)
5 points by syllogism 3y ago|0 comments
58.
Towards a Tagalog NLP Pipeline(ljvmiranda921.github.io)
4 points by syllogism 4y ago|0 comments
59.
by syllogism 4y ago
It's a venture fund, as a lot of large companies have. I don't really get the argument for it, I think it's pretty dumb overall. But it's not that uncommon.
60.
by syllogism 4y ago
The paper trains an XGBoost model to predict investment outcomes for startup investments from the time period 2014-2016. The model is trained on investments prior to 2014, and uses features from Pitchbook. It says the most important feature

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