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Ask HN: If we train an LLM with “data” instead of “language” tokens

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Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#31

Post by croes »

jmbiven wrote: post #27

Jordan Volz wrote an article speculating about this earlier this year. First time I heard the term Large Data Model, which I love and plan on using. https://medium.com/@jordan_volz/who-owns-the-future-looking-... "I won’t claim it’s simple, but as we’ve built AI that can understand human language, we can similarly build AI that understands data...Instead of building a large language model, we instead can build large…

But how do we communicate with an entity that understands data but not human language?

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#32

Post by shwaj »

kylebenzle wrote: post #26
binarymax wrote: post #17

Earlier quoted context omitted.

Sorry, but are you suggesting that transformers have been used by quants prior to the language application? LLM these days implies a transformer architecture.

Yes [1] (1) https://arxiv.org/pdf/2208.08300.pdf Aug 2022, TRANSFORMER-BASED DEEP LEARNING MODEL FOR STOCK PRICE PREDICTION International Journal of Computational Intelligence and Applications Something similar was an exercise/project in the NYCDSA "Data Science Boot Camp" I did nearly 2 years ago. I am fairly sure that applying these models to language was secondary, right?

The transformer model was introduced in the 2017 paper “Attention is all you need”, which applied it to machine translation of human language.

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#33

Post by arbuge »

croes wrote: post #31
jmbiven wrote: post #27

Jordan Volz wrote an article speculating about this earlier this year. First time I heard the term Large Data Model, which I love and plan on using. https://medium.com/@jordan_volz/who-owns-the-future-looking-... "I won’t claim it’s simple, but as we’ve built AI that can understand human language, we can similarly build AI that understands data...Instead of building a large language model, we instead can build large…

But how do we communicate with an entity that understands data but not human language?

It doesn't seem to me that that's a problem. You just put data in and expect data out.

For example, a weather continuation model could take in all the available weather data up to the present moment and provide a continuation of that, i.e. a weather forecast.

Likewise financial market predictions etc.

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#34

Post by mdp2021 »

phillipcarter wrote: post #11
chartpath wrote: post #7

Earlier quoted context omitted.

Just like NNs did to symbolic AI which is sorely missed to build explainable and ethical systems.

> ethical systems I don't think symbolic AI has much to do with ethics? My impression is that ethics in this space are primarily concerned with the application of these technologies, especially when their deficiencies and biases are known.

The poster probably referred reliability as an ethical value; direct choice in building the system as an ethical value because embedded with your decisions hence responsibility; and conversely proposing "black-boxes" as an ethical fault.

There are several possible reasons for this latter: not only that you should not be contented until you "could warranty" for "your" system, but also the possible outcomes. For example, yesterday I submitted a

“Are you kidding, carjacking?” – The problem with facial recognition in policing - https://news.ycombinator.com/item?id=37138229

where reporting is made of law reliability systems that have false positives of bewildering error and heavy consequences.

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#35

Post by belter »

iroddis wrote: post #10
tomohelix wrote: post #5

RLHF is a very important part of the training process. Without it, the LLMs would definitely behave much more similar to a glorified autocomplete than something resembling "intelligence". So the answer is that if you just feed it a bunch of data, it might be good at spitting out some similarly looking data but ultimately has little use. If you manage to get some experts sitting with it and give it instructions and ex…

To save others from searching, RLHF [1] is Reinforcement Learning from Human Feedback [1] https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu...

And to put it in context...RLHF is the special sauce behind ChatGPT

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#36

Post by claytonjy »

kylebenzle wrote: post #26
binarymax wrote: post #17

Earlier quoted context omitted.

Sorry, but are you suggesting that transformers have been used by quants prior to the language application? LLM these days implies a transformer architecture.

Yes [1] (1) https://arxiv.org/pdf/2208.08300.pdf Aug 2022, TRANSFORMER-BASED DEEP LEARNING MODEL FOR STOCK PRICE PREDICTION International Journal of Computational Intelligence and Applications Something similar was an exercise/project in the NYCDSA "Data Science Boot Camp" I did nearly 2 years ago. I am fairly sure that applying these models to language was secondary, right?

No, language models were not a secondary application transformers. From the abstract of the paper you posted:

> The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task at DSE.

In general, all the fancy DL stuff from the last decade started on either images (e.g. CNNs) or text (e.g. RNNs, transformers). Applying them to tabular data is generally an afterthought, and for time series in particular is rarely worth the time and compute.

The paper you posted is not very good and should not be used to inform your own work. It includes no comparisons to baseline models, and even the abstract describes the results as "acceptable".

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#37

Post by MichaelZuo »

arbuge wrote: post #33
croes wrote: post #31

Earlier quoted context omitted.

But how do we communicate with an entity that understands data but not human language?

It doesn't seem to me that that's a problem. You just put data in and expect data out. For example, a weather continuation model could take in all the available weather data up to the present moment and provide a continuation of that, i.e. a weather forecast. Likewise financial market predictions etc.

That's a good point, Math is just as much a communicable language as English, French, Japanese, etc... maybe even more so according to some formal definitions.

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#38

Post by optimalsolver »

croes wrote: post #31
jmbiven wrote: post #27

Jordan Volz wrote an article speculating about this earlier this year. First time I heard the term Large Data Model, which I love and plan on using. https://medium.com/@jordan_volz/who-owns-the-future-looking-... "I won’t claim it’s simple, but as we’ve built AI that can understand human language, we can similarly build AI that understands data...Instead of building a large language model, we instead can build large…

But how do we communicate with an entity that understands data but not human language?

Send it a sequence of raw UTF bytes and see what it replies with.

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#39

Post by BreadPants »

arbuge wrote: post #33
croes wrote: post #31

Earlier quoted context omitted.

But how do we communicate with an entity that understands data but not human language?

It doesn't seem to me that that's a problem. You just put data in and expect data out. For example, a weather continuation model could take in all the available weather data up to the present moment and provide a continuation of that, i.e. a weather forecast. Likewise financial market predictions etc.

How about a multi-headed approach. LDMs specializing in certain data operating under one LLM that acts as our point of access?

Re: Ask HN: If we train an LLM with “data” instead of “language” tokens

#40

Post by _8j50 »

I hope this doesn't become a thing, ML is already being abused to track people. I have no problem against the technology but with the lack of laws to address its abuse.

Imagine large models trained on ad targeting+click data, cctv, wifi+location data, sensor and audio crap from phones, geospatial/streetview, widearea sat photos,etc... and you combine that with LLMs

Yeah, very powerful but not only can it be abused, it can't be scrutinized and unlike LLM data, other types of data (specially the private dataset kind) can't be publicly and independently corrobarates in most cases I'd imagine.

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