Advanced neural networks, reminiscent of Giant Language Fashions (LLMs), endure very often from interpretability challenges. One of the vital essential causes for such problem is superposition — a phenomenon of the neural community having fewer dimensions than the variety of options it has to signify. For instance, a toy LLM with 2 neurons has to current 6 completely different language options. Consequently, we observe typically {that a} single neuron must activate for a number of options. For a extra detailed rationalization and definition of superposition, please check with my earlier blog post: “Superposition: What Makes it Troublesome to Clarify Neural Community”.
On this weblog put up, we take one step additional: let’s attempt to disentangle some fsuperposed options. I’ll introduce a technique referred to as Sparse Autoencoder to decompose complicated neural community, particularly LLM into interpretable options, with a toy instance of language options.
A Sparse Autoencoder, by definition, is an Autoencoder with sparsity launched on objective within the activations of its hidden layers. With a relatively easy construction and lightweight coaching course of, it goals to decompose a fancy neural community and uncover the options in a extra interpretable approach and extra comprehensible to people.
Allow us to think about that you’ve a skilled neural community. The autoencoder just isn’t a part of the coaching means of the mannequin itself however is as a substitute a post-hoc evaluation software. The unique mannequin has its personal activations, and these activations are collected afterwards after which used as enter information for the sparse autoencoder.
For instance, we suppose that your unique mannequin is a neural community with one hidden layer of 5 neurons. Moreover, you might have a coaching dataset of 5000 samples. It’s a must to gather all of the values of the 5-dimensional activation of the hidden layer for all of your 5000 coaching samples, and they’re now the enter on your sparse autoencoder.
The autoencoder then learns a brand new, sparse illustration from these activations. The encoder maps the unique MLP activations into a brand new vector area with larger illustration dimensions. Trying again at my earlier 5-neuron easy instance, we’d take into account to map it right into a vector area with 20 options. Hopefully, we’ll receive a sparse autoencoder successfully decomposing the unique MLP activations right into a illustration, simpler to interpret and analyze.
Sparsity is a crucial within the autoencoder as a result of it’s obligatory for the autoencoder to “disentangle” options, with extra “freedom” than in a dense, overlapping area.. With out existence of sparsity, the autoencoder will most likely the autoencoder may simply study a trivial compression with none significant options’ formation.
Language mannequin
Allow us to now construct our toy mannequin. I urge the readers to notice that this mannequin just isn’t sensible and even a bit foolish in apply however it’s adequate to showcase how we construct sparse autoencoder and seize some options.
Suppose now now we have constructed a language mannequin which has one explicit hidden layer whose activation has three dimensions. Allow us to suppose additionally that now we have the next tokens: “cat,” “pleased cat,” “canine,” “energetic canine,” “not cat,” “not canine,” “robotic,” and “AI assistant” within the coaching dataset and so they have the next activation values.
information = torch.tensor([
# Cat categories
[0.8, 0.3, 0.1, 0.05], # "cat"
[0.82, 0.32, 0.12, 0.06], # "pleased cat" (much like "cat")
# Canine classes
[0.7, 0.2, 0.05, 0.2], # "canine"
[0.75, 0.3, 0.1, 0.25], # "loyal canine" (much like "canine")# "Not animal" classes
[0.05, 0.9, 0.4, 0.4], # "not cat"
[0.15, 0.85, 0.35, 0.5], # "not canine"
# Robotic and AI assistant (extra distinct in 4D area)
[0.0, 0.7, 0.9, 0.8], # "robotic"
[0.1, 0.6, 0.85, 0.75] # "AI assistant"
], dtype=torch.float32)
Development of autoencoder
We now construct the autoencoder with the next code:
class SparseAutoencoder(nn.Module):
def __init__(self, input_dim, hidden_dim):
tremendous(SparseAutoencoder, self).__init__()
self.encoder = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.Linear(hidden_dim, input_dim)
)def ahead(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return encoded, decoded
In line with the code above, we see that the encoder has a just one absolutely related linear layer, mapping the enter to a hidden illustration with hidden_dim and it then passes to a ReLU activation. The decoder makes use of only one linear layer to reconstruct the enter. Word that the absence of ReLU activation within the decoder is intentional for our particular reconstruction case, as a result of the reconstruction may include real-valued and probably unfavourable valued information. A ReLU would quite the opposite power the output to remain non-negative, which isn’t fascinating for our reconstruction.
We prepare mannequin utilizing the code under. Right here, the loss operate has two components: the reconstruction loss, measuring the accuracy of the autoencoder’s reconstruction of the enter information, and a sparsity loss (with weight), which inspires sparsity formulation within the encoder.
# Coaching loop
for epoch in vary(num_epochs):
optimizer.zero_grad()# Ahead move
encoded, decoded = mannequin(information)
# Reconstruction loss
reconstruction_loss = criterion(decoded, information)
# Sparsity penalty (L1 regularization on the encoded options)
sparsity_loss = torch.imply(torch.abs(encoded))
# Whole loss
loss = reconstruction_loss + sparsity_weight * sparsity_loss
# Backward move and optimization
loss.backward()
optimizer.step()
Now we are able to take a look of the outcome. We have now plotted the encoder’s output worth of every activation of the unique fashions. Recall that the enter tokens are “cat,” “pleased cat,” “canine,” “energetic canine,” “not cat,” “not canine,” “robotic,” and “AI assistant”.
Regardless that the unique mannequin was designed with a quite simple structure with none deep consideration, the autoencoder has nonetheless captured significant options of this trivial mannequin. In line with the plot above, we are able to observe not less than 4 options that look like discovered by the encoder.
Give first Function 1 a consideration. This feautre has large activation values on the 4 following tokens: “cat”, “pleased cat”, “canine”, and “energetic canine”. The outcome means that Function 1 may be one thing associated to “animals” or “pets”. Function 2 can be an attention-grabbing instance, activating on two tokens “robotic” and “AI assistant”. We guess, subsequently, this characteristic has one thing to do with “synthetic and robotics”, indicating the mannequin’s understanding on technological contexts. Function 3 has activation on 4 tokens: “not cat”, “not canine”, “robotic” and “AI assistant” and that is presumably a characteristic “not an animal”.
Sadly, unique mannequin just isn’t an actual mannequin skilled on real-world textual content, however relatively artificially designed with the idea that comparable tokens have some similarity within the activation vector area. Nonetheless, the outcomes nonetheless present attention-grabbing insights: the sparse autoencoder succeeded in displaying some significant, human-friendly options or real-world ideas.
The straightforward outcome on this weblog put up suggests:, a sparse autoencoder can successfully assist to get high-level, interpretable options from complicated neural networks reminiscent of LLM.
For readers interested by a real-world implementation of sparse autoencoders, I like to recommend this article, the place an autoencoder was skilled to interpret an actual massive language mannequin with 512 neurons. This research supplies an actual utility of sparse autoencoders within the context of LLM’s interpretability.
Lastly, I present right here this google colab notebook for my detailed implementation talked about on this article.
