So cool. The only moat I can think of for such a company would be proprietary fact loras- basically licensing a modern ai encyclopedia.
Anyway, really nice idea.
Embedding a description of the Lora and using RAG to pull the nearest Loras in the embedding space is where my mind goes; it’s super extensible, minimal additional training for customer use cases, and the way the Loras probably work it’s not terrible to pull a few extras.
Anyway I just speculate —- no idea what they’re actually doing on the backend.
The idea from LoRA[1] is to take pre-trained, dense weights W_0 for a model and adapt them to new training data by using the weights W = W_0 + BA for inference. The key is that the matrices A and B have a very low rank[2] compared to W_0. Training is then done by treating W_0 as constant, and only updating the A and B matrices.
The idea from MoE[3] is to pick just a few "experts" from a large number using Softmax, and insert them between other layers in a neural network. The "experts" can be just some simple matrices or neural networks in their own right.
The Lamini model seems to combine these ideas, where they use several "experts" layered between the BA matrices. However instead of just a simple Softmax to select the "experts" they use (chunked?) cross-attention[4], and a much larger number of "experts" compared to the MoE paper. From what I gather the cross-attention allows the expert selection to react to the context, unlike the more naive plain Softmax gating approach.
Similarly to LoRA, they train the Lamini model by keeping the pre-trained LLM weights constant. From what I can gather they do a bit of training on the cross-attention layer but then freeze that too, to avoid the cross-attention layer favoring the same "experts" all the time.
The idea then is to train the "experts" and the LoRA layer on facts until the combined model (W above) gets each fact correct (zero loss).
Thus when the model "sees" a keyword in a sentence, a set of "experts" will steer/adjust the output of the pre-trained LLM to output the correct fact. Or at least that's how I imagine it works.
What's less clear to me is what exactly the "experts" are and how they are combined.
Since they're used to adjust the weights of the combined model, I it makes the most sense to my un-trained eye that they're simple matrices as mentioned in the MoE paper. Given they're layered between the low-rank portions of the LoRA section, they're necessarily relatively small matrices, so having millions of these "expert" matrices doesn't add too many parameters overall.
And while they're portrayed as stacked in the Lamini paper, suggesting matrix multiplication, matrices are generally not commutative[5]. So to the un-trained eye it seems likely the "experts" are simply added like in the MoE paper.
But yeah, I'm very much not an expert and the paper was more like a conference poster at best, lacking a lot of detail, so this might be all gibberish and I'd appreciate being corrected.
[1]: https://arxiv.org/abs/2106.09685
[2]: https://en.wikipedia.org/wiki/Rank_factorization
[3]: https://arxiv.org/abs/1701.06538
In this case, the experts could literally be routing / weighting of the LoRas, so it could be a 1x100k (or 1mm or whatever) vector , binary, maybe for simplicity and size. Or floats that are capped to 1/0 at inference time, but trained as floats.
The thing that’s a little weird to me is that you’d need to keep retraining the experts. But, I guess it may just be part of the pipeline for adding custom knowledge to the system.
If "AI" simply allowed people to search data without having to structure it then apply very rigid search terms, that would be in a win in my book.
How do I multiply positive numbers and get something smaller?
Is "1/10th" or "90% less" not better arithmetic?
Maybe I should have done more gooder at math but it hurts my ears (eyes).
fractions are gonna blow your mind
I've asked around and people generally seem to prefer the bigger, sexier number. I don't care too much either way so I just go with the flow. Shrug.
eg. if hallucinations occur roughly 1 in 20 prompts then 10x fewer is 1 in 200 prompts, rather than 0.1 in 20 prompts.
I am also annoyed by most modern tech marketing using percentages incorrectly and inconsistently. But 150% is a bigger number than 1.5x so I suppose their hands are tied.
More is addition, times more is multiplication (or 1 plus multiplication, oops).
I don't think anyone says "twice fewer". Or "twice more" when talking about quantities.
On the other side, it seems clear that the scientific community was able to deduce the intended meaning of "10x fewer".
> At inference time, the model retrieves the most relevant experts at each layer and merges back into the base model to respond to the user query.
The paper says:
> At inference time, only the relevant experts are retrieved from the index, allowing the LLM to store a large number of facts while maintaining low inference latency. We use specialized GPU kernels written in Triton Tillet et al. (2019) to accelerate the lookup of experts.
...but darned if I can understand from either what they're actually doing when they say that.
Why do you need a custom GPU kernel for this outside of the normal NN layers?
Can anyone see an explanation of how they pick which expert to use?
They do reference some papers I’m not familiar with and say their method is “similar”.
If you check the huggingface page mentioned in a footnote, they have two directories: one for a model, and the other which contains a FAISS index. Although in the paper they say they use cross attention, so I have no idea how those could be combined.
I consider that paper an early draft - hot off the press so to say - it needs review & editing before we would submit it to a conference. I tend to prefer a few rounds of open review before a final submission these days anyways - so appreciate the feedback
I think the main idea should be reproducible - you can repeat the randomization and generalization tests with any LLM and get similar training curves and eval results - it just wouldn’t be efficient.
We have tried it on about 5 real customer use cases with different facts and good success. Obviously we can’t publish customer data to reproduce which is why we focused on the randomization tests in the paper .
There are also some missing hyper parameters from the appendix as well we will add eventually
Let's say I have a counter value, X = 100. I reduce that to 10. How can I phrase that in English?
"Value reduced to 10% of original value"
"Value reduced by 90%“
"New value is one tenth the original Value"
Using multiplication with a positive integer and saying "less" just seems incomprehensible when it flies by during a sentence and I can't stop myself from mentally saying "No", like Neo at the end of the Matrix when the three agents fire a volley of bullets at him down the corridor. "No, this sentence stops right here while I pick it apart"
Also the most direct translation is "value reduced by a factor of ten".
"x" means factor, "fewer" or "less" means reduced.
The first sentence seems fine to me. The contraction much less so.
Sounds good as non-native.
Being able to say "you are wrong, taking over the world is a bad idea" and have the model respond with "oh you are completely right, I am very sorry for that" was our first line of defense.
I wonder if this model will argue insistently that you are wrong if you try and tell it that 1+1=3, and if so, whether that expands to philosophical issues such as the model arguing back at you based on its formed opinions on ethics and history.
I couldn't tell how expensive this is up front, or what complexity it adds to the setup. Anyone know?
It's definitely an interesting idea but if you have to pay $100k for all that LoRA, what margins are left over?
Going from 50% "accurate" to 90% "accurate" may actually be more insidious because it changes the utility from being a coin flip to trying to determine which 10% is inaccurate, or downplaying the existence of inaccuracies because at 90% it is "mostly correct".
We can control how diverse or creative output is via temperature. I am assuming that these new models could work at higher temperatures (i.e. more creative) while maintaining factual accuracy for things you care about. Or alternatively, keep the same temperature but have far fewer hallucinations (i.e. wrong answers).
But true, would love to see this in OpenSource in the wild
