4 ms·

For fun, I pasted these into ChatGPT o4-mini-high and asked it for an opinion: data + plural = datasets data - plural = datum king - crown =

by n2d4 1y ago

For fun, I pasted these into ChatGPT o4-mini-high and asked it for an opinion: data + plural = datasets data - plural = datum king - crown = ruler king - princess = man king - queen = prince queen - king = woman king + queen = royalty boy + age = man man - age = boy woman - age = girl woman + age = elderly woman girl + age = woman girl + old = grandmother The results are surprisingly good, I don't think I could've done better as a human. But keep in mind that this doesn't do embedding math like OP! Although it does show how generic LLMs can solve some tasks better than traditional NLP. The prompt I used: > Remember those "semantic calculators" with AI embeddings? Like "king - man + woman = queen"? Pretend you're a semantic calculator, and give me the results for the following:

nbardy 1y ago
I hate to be pedantic, but the llm is definitely doing embedding math. In fact that’s all it does.
- n2d4 1y ago
  Sure! Although I think we both agree that the way those embeddings are transformed is significantly different ;) (what I meant to say is that it doesn't do embedding math "LIKE" the OP — not that it doesn't do embedding math at all.)
  - coolcase 1y ago
    Yeah we'd be impressed if an LLM calculated the product of a couple of 1000x1000 matrices.
franga2000 1y ago
This is an LLM approximating a semantic calculator, based solely on trained-in knowledge of what that is and probably a good amount of sample output, yet somehow beating the results of a "real" semantic calculator. That's crazy! The more I think about it the less surprised I am, but my initial thoughts were quite simply "now way" - surely an approximation of an NLP model made by another NLP model can't beat the original, but the LLM training process (and data volume) is just so much more powerful I guess...
- CamperBob2 1y ago
  This is basically the whole idea behind the transformer. Attention is much more powerful than embedding alone.
  - godelski 1y ago
    The transformers are initialized by embedding models... Your embedding model is literally the translation layer converting the text to numbers. The transformers are the main processing unit of the embeddings. You can even see some self-reflection in the model as the transformer is composed of attention and a MLP sub-network. The attention mechanism generates the interrelational dependence of the data and the MLP projects up into a higher dimension before coming down so that this can untangle these relationships. But the idea is that you just repeat this process over and over. The attention mechanism has the benefit over CNN models because it has a larger receptive field, so can better process long range relationships (long range being across the input data) where CNNs bias for local relationships.
refulgentis 1y ago
...welcome to ChatGPT, everyone! If you've been asleep since...2022? (some might say all an LLM does is embeddings :)
godelski 1y ago
> The results are surprisingly good, I don't think I could've done better as a human I'm actually surprised that the performance is so poor and would expect a human to do much better. The GPT model has embedding PLUS a whole transformer model that can untangle the embedded structure. To clarify some of the issues: data is both singular and plural, being a mass noun[0,1]. Datum is something you'll find in the dictionary, but not common in use[2]. The dictionary lags actual definitions. I mean words only mean what we collectively agree they mean (dictionary definitely helps with that but we also invent words all the time -- i.e. slang). I see how this one could trick up a human, feeling the need to change the output and would likely consult a dictionary but I don't think that's a fair comparison here as LLMs don't have these same biases. King - crown really seems like it should be something like "man" or "person". The crown is the manifestation of the ruling power. We still use phrases like "heavy is the head that wears the crown" in reference to general leaders, not just monarchs. king - princess I honestly don't know what to expect. Man is technically gender neutral so I'll take this one. king - queen I would expect similar outputs to the previous one. Don't quite agree here. queen - king I get why is removing royalty but given the previous (two) results I think is showing a weird gender bias. Remember that queen is something like (woman + crown) and king is akin to (man + crown). So subtracting should be woman - man. The others I agree with. These were actually done because I was quite surprised at the results and was thinking about the aforementioned gender bias. > But keep in mind that this doesn't do embedding math like OP! I think you are misunderstanding the architecture of these models. The embedding sub-network is the translation of text to numeric tokens. You'll find mention of the embedding sub-networks in both the GPT3[3] and GPT4 papers. Though they are given lower importance than other works. While much smaller than the main network, don't forget that embedding networks are still quite large. For the smaller models they constitute a significant part of the total parameter count[4] After the embedding sub-network is your main transformer network. The purpose of this network is to perform embedding math! It is just that the goal is to do significantly more complicated math. Remember, these are learnable mappings (see Optimal Transport). We're just breaking it down into their two main intermediate mappings. But the embeddings still end up being a bottleneck. It is your literal gateway from words to numbers. [0] https://en.wikipedia.org/wiki/Mass_noun https://en.wikipedia.org/wiki/Mass_noun [1] https://www.merriam-webster.com/dictionary/data https://www.merriam-webster.com/dictionary/data [2] https://www.sciotoanalysis.com/news/2023/1/18/this-data-or-these-data-which-is-correct https://www.sciotoanalysis.com/news/2023/1/18/this-data-or-t... [3] https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165 [4] https://arxiv.org/abs/2303.08774 https://arxiv.org/abs/2303.08774 [4] https://www.lesswrong.com/posts/3duR8CrvcHywrnhLo/how-does-gpt-3-spend-its-175b-parameters https://www.lesswrong.com/posts/3duR8CrvcHywrnhLo/how-does-g...
amdivia 1y ago
Can you do the same but each line is done in a seperate context?

by n2d4 1y ago

nbardy 1y ago

I hate to be pedantic, but the llm is definitely doing embedding math. In fact that’s all it does.

n2d4 1y ago
Sure! Although I think we both agree that the way those embeddings are transformed is significantly different ;) (what I meant to say is that it doesn't do embedding math "LIKE" the OP — not that it doesn't do embedding math at all.)
- coolcase 1y ago
  Yeah we'd be impressed if an LLM calculated the product of a couple of 1000x1000 matrices.

franga2000 1y ago

This is an LLM approximating a semantic calculator, based solely on trained-in knowledge of what that is and probably a good amount of sample output, yet somehow beating the results of a "real" semantic calculator. That's crazy! The more I think about it the less surprised I am, but my initial thoughts were quite simply "now way" - surely an approximation of an NLP model made by another NLP model can't beat the original, but the LLM training process (and data volume) is just so much more powerful I guess...

CamperBob2 1y ago
This is basically the whole idea behind the transformer. Attention is much more powerful than embedding alone.
- godelski 1y ago
  The transformers are initialized by embedding models... Your embedding model is literally the translation layer converting the text to numbers. The transformers are the main processing unit of the embeddings. You can even see some self-reflection in the model as the transformer is composed of attention and a MLP sub-network. The attention mechanism generates the interrelational dependence of the data and the MLP projects up into a higher dimension before coming down so that this can untangle these relationships. But the idea is that you just repeat this process over and over. The attention mechanism has the benefit over CNN models because it has a larger receptive field, so can better process long range relationships (long range being across the input data) where CNNs bias for local relationships.

refulgentis 1y ago

...welcome to ChatGPT, everyone! If you've been asleep since...2022? (some might say all an LLM does is embeddings :)

godelski 1y ago

> The results are surprisingly good, I don't think I could've done better as a human I'm actually surprised that the performance is so poor and would expect a human to do much better. The GPT model has embedding PLUS a whole transformer model that can untangle the embedded structure. To clarify some of the issues: data is both singular and plural, being a mass noun[0,1]. Datum is something you'll find in the dictionary, but not common in use[2]. The dictionary lags actual definitions. I mean words only mean what we collectively agree they mean (dictionary definitely helps with that but we also invent words all the time -- i.e. slang). I see how this one could trick up a human, feeling the need to change the output and would likely consult a dictionary but I don't think that's a fair comparison here as LLMs don't have these same biases. King - crown really seems like it should be something like "man" or "person". The crown is the manifestation of the ruling power. We still use phrases like "heavy is the head that wears the crown" in reference to general leaders, not just monarchs. king - princess I honestly don't know what to expect. Man is technically gender neutral so I'll take this one. king - queen I would expect similar outputs to the previous one. Don't quite agree here. queen - king I get why is removing royalty but given the previous (two) results I think is showing a weird gender bias. Remember that queen is something like (woman + crown) and king is akin to (man + crown). So subtracting should be woman - man. The others I agree with. These were actually done because I was quite surprised at the results and was thinking about the aforementioned gender bias. > But keep in mind that this doesn't do embedding math like OP! I think you are misunderstanding the architecture of these models. The embedding sub-network is the translation of text to numeric tokens. You'll find mention of the embedding sub-networks in both the GPT3[3] and GPT4 papers. Though they are given lower importance than other works. While much smaller than the main network, don't forget that embedding networks are still quite large. For the smaller models they constitute a significant part of the total parameter count[4] After the embedding sub-network is your main transformer network. The purpose of this network is to perform embedding math! It is just that the goal is to do significantly more complicated math. Remember, these are learnable mappings (see Optimal Transport). We're just breaking it down into their two main intermediate mappings. But the embeddings still end up being a bottleneck. It is your literal gateway from words to numbers. [0] https://en.wikipedia.org/wiki/Mass_noun https://en.wikipedia.org/wiki/Mass_noun [1] https://www.merriam-webster.com/dictionary/data https://www.merriam-webster.com/dictionary/data [2] https://www.sciotoanalysis.com/news/2023/1/18/this-data-or-these-data-which-is-correct https://www.sciotoanalysis.com/news/2023/1/18/this-data-or-t... [3] https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165 [4] https://arxiv.org/abs/2303.08774 https://arxiv.org/abs/2303.08774 [4] https://www.lesswrong.com/posts/3duR8CrvcHywrnhLo/how-does-gpt-3-spend-its-175b-parameters https://www.lesswrong.com/posts/3duR8CrvcHywrnhLo/how-does-g...

amdivia 1y ago

Can you do the same but each line is done in a seperate context?