[HN Gopher] Large Language Models As General Pattern Machines
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Large Language Models As General Pattern Machines
Author : optimalsolver
Score : 69 points
Date : 2023-08-20 10:12 UTC (12 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| asklotsogque wrote:
| Amazing how much the mainstream media are still obsessed with
| LLMs and factual accuracy. I prefer to see them as the worlds
| greatest innovation as lateral thinking machines ie pattern A
| applied over pattern B to give a credible pattern C.
| hackernoteng wrote:
| I think there is more innovation to happen with LLM output, for
| example leveraging the attention weights to give information on
| what the key events in history should be paid attention to
| given a sequence of current events, etc. More predictive
| information beyond just generated text responses
| ("completions").
| monkeydust wrote:
| A useful feature of RAG is actual text citations, found it
| useful in steering the user to develop their own reading list
| for further follow ups
| martindbp wrote:
| Remember the outcry against using Wikipedia as a source in
| school-work etc? Pretty convinced that the fear that "LLMs just
| make stuff up" will gradually go away as they get better.
|
| Also, at the moment you should view e.g. ChatGPT as your
| autistic well-read friend who really do know quite a lot of
| things, but only approximately, and is prone to make things up
| instead of being found out for not knowing. It's OK to ask your
| autistic friend how electricity works, but don't expect him to
| correctly cite the titles and authors of relevant research
| papers. Just common sense stuff if you really think of the LLM
| as a brain of compressed information and not a search engine.
| curtisf wrote:
| LLMs are, and will continue to be, used for applications where
| factual accuracy is important. The decisions that LLM outputs
| prompt will soon be having big impacts in people's actual
| lives, if they aren't already.
|
| The appearance of credibility of the output makes this much
| worse, since inevitably decisions will be deferred to LLMs that
| they are not sufficiently accurate enough for.
| hackernoteng wrote:
| Real world industry wont stand for "hallucinated" outputs,
| unless there is more innovation around UI/UX on outputs. For
| example, no way lawyers/bankers/doctors are going to use LLM
| in their current forms and limitations if they can't trust
| the outputs.
| JPLeRouzic wrote:
| If you have employees, you also need to create transverse
| structures ( _make people work in teams, set check lists,
| QA, HR, accounting, create corporate charters on gender
| equality and many other topics, tell many "statements that
| ..." or "our mission is ...", create corporate culture, _)
| because you can't trust humans employees at 100%.
|
| Actually some of banks biggest failures were when only a
| few and in some cases only one person was in charge.
|
| https://en.wikipedia.org/wiki/List_of_corporate_collapses_a
| n...
|
| How is that different from LLMs?
| captn3m0 wrote:
| Professional norms differ between countries. What might be
| unthinkable for a doctor in US might be meh for a
| professional in India.
|
| For eg, a judge in India used ChatGPT for a bail hearing.
| https://www.livemint.com/news/india/this-indian-court-has-
| us...
| npsomaratna wrote:
| Disagree. As a lawyer I use LLMs with RAG to help me
| surface information all the time. Often, this allows me to
| find niche case law that I just wouldn't have had the time
| to find on my own. However, I double-check everything, and
| read all the original sources.
|
| LLMs are best treated as the AI equivalent of a human
| assistant who is knowledgeable and fast, but also
| inexperienced, and thus, prone to making mistakes. You
| won't throw out the work of such an assistant-it'll still
| save you hours of effort. However, you won't take the work
| at face value either.
| squeaky-clean wrote:
| I have a friend in law school at the moment, and while he
| obviously can't use AI for school, multiple professors of
| his have recommended he get familiar with using them now
| so he'll be efficient at using them after passing the
| bar.
| jebarker wrote:
| I use LLMs for helping with coding it's proving
| invaluable. I see it very similarly, an inexperienced
| assistant with very broad knowledge. I also find that
| they don't make too many mistakes in tasks such as
| refactoring or finding bugs, it's when you ask it to just
| wholesale generate code for you that you hit problems. If
| I were to just take the code as is, not test it and not
| check I understand it then use it it's me that would be
| making the mistake not the LLM.
| arrowsmith wrote:
| What's RAG?
| simonw wrote:
| Retrieval Augmented Generation.
|
| It's the trick where you take a question from a user,
| search for documents that match that question, stuff as
| many of the relevant chunks of content from those
| documents as you can into the prompt (usually 4,000 or
| 8,000 tokens, but Claude can go up to 100,000) and then
| say to the LLM "Based on this context, answer this
| question: QUESTION".
|
| I wrote about one way to implement that here:
| https://simonwillison.net/2023/Jan/13/semantic-search-
| answer...
| gryn wrote:
| funny you mention lawyers when just not long ago some of
| them made the news because they trusted chatGPT which gave
| them a imaginary precedent.
|
| just because a small percentage technically oriented people
| know the limitations of LLMs doesn't mean that the rest do.
|
| people have a tendency to anthropomorphize things, so they
| naturally think that LLMs think in the same way other
| humans they know think.
|
| edit: first link I found about it, there was also some
| posts about it here in HN.
| https://www.abc.net.au/news/2023-06-09/lawyers-blame-
| chatgpt...
| naasking wrote:
| > Real world industry wont stand for "hallucinated" outputs
|
| Of course they will, if the other benefits are large
| enough. Checking factual accuracy of a large corpus can
| often be considerably simpler than generating the large
| corpus to begin with.
| ryanklee wrote:
| Real world industry already relies heavily on hallucinated
| outputs. They are called humans.
|
| One of the main differences between people who see the
| astonishing value of LLMs right now and people who are some
| combo of skeptical, dismissive, and indignant, is the
| expectation that for something to be a valuable source of
| information, it has to be factually accurate every time.
|
| The entirety of civilization has been built on the back of
| inaccurate sources of information.
|
| That will never change and it absolutely can't, because (1)
| factual accuracy is not something that can be determined by
| consensus in a variety of significant cases, and (2)
| factual accuracy as a concept itself does not have a
| consensus definition, operationally or in the abstract.
|
| Absolutely frustrating to see these topics addressed as if
| thousands of years of intense thinking around truth and
| factual accuracy has not taken place.
|
| The results of those inquiries do not support the basic
| assumptions of these conversations (i.e. that factual
| accuracy is amenable to exhaustive algorithmic
| verification).
| BoorishBears wrote:
| That's a product problem, not an ML problem.
|
| If I'm making a tool for doctors, I don't need to surface the
| exact scientific fact the LLM recalled from memory, I can
| design an interface that surfaces verbatim text from sources
| based on the LLM's understanding of the situation.
|
| No serious product should be surfacing a ChatGPT style chat
| window to the user, it's a poor UX anyways with awful
| discoverability.
| flangola7 wrote:
| But how long will factual accuracy remain below human level?
| I expect the incorrect information issue to be a short term
| problem. That new GPT-4 based legal AI the BigLaw firms are
| signing up for already produces some documents with a lower
| error rate than a human lawyer.
|
| Perfection isn't necessary, it only needs to be better than
| the average human. In a year or two I expect those kinks will
| be worked out for countless job tasks.
| golol wrote:
| This is the real breakthrough of LLMs imo. Karpathy had a series
| of tweest about this a year ago. It makes sense: Prettained LLMs
| learn to continue sequences of tokens. These are language tokens
| but the language is so complex and rich that a kind of general
| pattern recognition and continuation ability is learned. I think
| finetuning and alignment destroys a large part of this in-context
| learning, so we are currently not focusing on this ability. In
| the future we will train on all kinds of token sequences.
| api wrote:
| If we can analyze these machines deeply and understand how they
| are doing this, is it possible that we can extract some kind of
| general purpose pattern recognition and manipulation algorithm
| or set of algorithms?
|
| Are we just inductively generating algorithms that could in
| fact run more powerfully and efficiently if they were
| "liberated" from the sea of matrix math in which they are
| embedded?
|
| Or is the sea of matrix math fundamental to how these things
| work?
| golol wrote:
| I don't think there is a magic alrogithm at play. I think
| it's all about studying the data - the geometry of the data
| manifold. In the case of a transformer more precisely
| learning the geometry of the stochastic process of tokens.
| Common "patterns", such as repetition or symmetry, appear
| there and can be learned. I think like learning eigenvectors
| in PCA you just learn more and more patterns and combinations
| of patterns. Then at some point that starts looking like
| pattern recognition.
| jncfhnb wrote:
| The sea of matrix math is the fundamental way these things
| work. You could grossly simplify individual patterns to small
| explicit algorithms but this is the way for general patterns.
| Compress your information, and then synthesize its new
| information with your understanding of the world into
| insights.
| GaggiX wrote:
| Looking at the ARC problems that the model didn't solve correctly
| I honestly have no idea in some of done on how the model of wrong
| or what should have been the solution given the train example.
|
| How strong is a human being on this challenge?
| neoneye2 wrote:
| There are only a few example images. It could be interesting
| seeing more examples of good/bad predictions.
| antognini wrote:
| It's quite hard. You can download the dataset here [1] and it
| comes with a little webpage so that you can try it yourself.
|
| It's worth noting that you are allowed to make three guesses.
|
| [1]: https://github.com/fchollet/ARC
| Xcelerate wrote:
| I always thought better sequence prediction was the natural
| prerequisite to AGI, given that it's kind of the whole foundation
| of algorithmic information theory (specifically, the upper limit
| to prediction is a prefix-free universal Turing machine that
| maximally compresses an input sequence and is allowed to continue
| running once it has reproduced the given input sequence).
|
| It's kind of funny to me that the general public seems to be
| recognizing this in a backward fashion -- "oh hey LLMs can be
| used for other stuff too!" I would be extremely surprised if any
| LLM researcher hasn't heard of Solomonoff induction though.
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