While reviewing the Transformer architecture, I realized something I didn't expect, which is that :

  • the positional encoding is summed to the word embeddings
  • rather than concatenated to it.

positional encoding summed to word embedding


Based on the graphs I have seen wrt what the encoding looks like, that means that :

  • the first few bits of the embedding are completely unusable by the network because the position encoding will distort them a lot,
  • while there is also a large amount of positions in the embedding that are only slightly affected by the positional encoding (when you move further towards the end).

graph shows positional encoding affects firsts logits a lot, last logits hardly not


So, why not instead have smaller word embeddings (reduce memory usage) and a smaller positional encoding retaining only the most important bits of the encoding, and instead of summing the positional encoding of words keep it concatenated to word embeddings?

  • $\begingroup$ I was also curious about this, have you figured it out? $\endgroup$
    – Lee MJ
    Commented Feb 1, 2020 at 14:34
  • $\begingroup$ @LeeMJ: No, I did not. $\endgroup$ Commented Feb 4, 2020 at 11:26
  • $\begingroup$ Have you figured it out now? $\endgroup$ Commented May 25, 2020 at 18:16
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    $\begingroup$ Is anyone aware of any papers where they tried concatenation instead of adding? $\endgroup$ Commented Feb 24, 2021 at 16:29
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    $\begingroup$ Just FYI: I've heard rumors that concatenation can at times be superior to addition, in part because concatenation does not change the number of learnable parameters too much (it just doubles the size of the embedding layers, but doesn't change the computation required within the core attention process.), and indeed, in an as-yet unpublished study, we did find an approach that concatenated to yield better performance without significantly increasing the size of the model overall or the amount of computation required to proccesss it. $\endgroup$ Commented Feb 13 at 21:30

8 Answers 8


When you concatenate, you have to define a priori the size of each vector to be concatenated. This means that, if we were to concatenate the token embedding and the positional embedding, we would have to define two dimensionalities, $d_t$ for the token and $d_p$ for the position, with the total dimensionality $d = d_t + d_p$, so $d>d_t$ and $d>d_p$. We would be decreasing the total size we devote to tokens in favor of positional information.

However, adding them together is potentially a super case of the concatenation: imagine that there is an ideal split of $d$ into $d_t$ and $d_p$ in terms of minimizing the loss; then, the training could converge to position vectors that only take $d_t$ elements, making the rest zero, and the positions were learned and happened the same, taking the complementary $d_p$ elements and leaving the rest to zero.

Therefore, by adding them, we leave the optimization of the use of the $d$ dimensions to the optimization process, instead of assuming there is an optimal partition of the vector components and setting a new hyperparameter to tune. Also, the use of the vector space is not restricted by a hard split in the vector components, but takes the whole representation space.

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    $\begingroup$ This would make sense for learned positional encoding. What about the sine/cosine encoding? Does it just rely on the fact that nothing much is happening in dimensions beyond the first few? $\endgroup$
    – max
    Commented Feb 24, 2021 at 5:56
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    $\begingroup$ While the equivalence of concatenation and addition may only apply to learned positional encoding, I think that the general optimization of the representation space does apply to fixed encodings as well (although the optimization only happens in the token embeddings). I don't think the picture is correct (it has changed in the referenced tutorial) $\endgroup$
    – noe
    Commented Feb 24, 2021 at 8:24
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    $\begingroup$ maybe a stupid question, but why this addition doesn't spoil the embedding - like we had word king, add this pattern and receive slave ? $\endgroup$ Commented Feb 25, 2021 at 21:39
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    $\begingroup$ If such a thing would happen, the final loss would be bad. The training aims at improving the loss, and therefore prevents that situation from happening. $\endgroup$
    – noe
    Commented Feb 25, 2021 at 21:45
  • $\begingroup$ You mentioned adding them, concatenating them. How about other ways like vector-matrix multiplication? It's a linear transformation. Theroretically, can this be used to integrate positional information as well? Is it a bad approach because of too many parameters (one matrix per position)? $\endgroup$ Commented Dec 3, 2022 at 9:48

the first few bits of the embedding are completely unusable by the network because the position encoding will distort them a lot

This confused me very much at first because I was thinking of the model using a pre-trained word embedding. And then an arbitrary initial chunk of that embedding gets severely tampered with by the positional encoding.

However, in the original transformer model at least, the embedding was trained from scratch, so this does not apply. An initial chunk of the overall embedding will be used for positional information, and the rest will be used for word information.

This still doesn't explain why we use this method instead of concatenation -- see the other answers for that -- but it does explain why the method isn't crazy.

That said, it may be that the method works well even with pre-trained word embeddings, I don't know. If so, it's hard to explain.

  • $\begingroup$ This was also a hunch of mine (to also resolve my confusion). I think this is probably why there is a lot of confusion in the discussion at large, with people unknowingly approaching the discussion with or without this assumption. $\endgroup$ Commented Jan 4 at 22:03
  • $\begingroup$ My suspicion is that using a pre-trained embedder and then adding positional encoding would throw off the attention dot-product calculation, since the existing keys vector/matrix wouldn't be calibrated to that. $\endgroup$ Commented Jan 4 at 22:19

The confusion here is that we believe positional embedding is a more complicated version of adding positional information to the word embedding; however, it is not actually. Adding new dimensions to each embedding increases the dimensionality of the problem. On the other hand, please note that the added positional embedding is (almost) static, as shown in this image for a 2D positional embedding:

enter image description here

The added positional embeddings are the same for all the inputs, and the transformer can separate the positional information from the actual word embedding through the training process. Therefore, the positional embedding doesn't mess with the word embedding information, and adding them is a more efficient way of adding the positional information that concatenates them.

  • $\begingroup$ it is possible that embed("foo") != embed("bar"), however, embed("foo") + p1 == embed("bar") + p2. Thought, the embedding is learned, the foo and bar will not appear in all possible position, thus the network has no way to separate them when this happens occasionally. $\endgroup$
    – Wang
    Commented Aug 3, 2023 at 15:42
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    $\begingroup$ How is the heatmap created here? can you share the snippet as well? $\endgroup$
    – Hossein
    Commented Dec 5, 2023 at 14:23
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    $\begingroup$ @Hossein The image is actually from this paper: "Exploring Recent Advancements of Transformer Based Architectures in Computer Vision" $\endgroup$ Commented Dec 5, 2023 at 16:34
  • $\begingroup$ @HamidMohammadi Thanks a lot. really appreciate it. $\endgroup$
    – Hossein
    Commented Dec 5, 2023 at 16:59

The following is conjecture, not fact.

If you look at how much each scalar in the the positional embedding vector changes as a function of position... you'll find that many of the scalars barely change at all. You can visualize this with any positional embedding plot, where the x axis is usually the [512] length of the vector, and the y axis is the position of the token.

For example, this image is from Jay Alammar's well regarded "The Illustrated Transformer"

enter image description here

Let's try to do this mathematically as well. The implementation of PE's that Jay references is at this Google GitHub repo:


Running the function on a PE/WE of length 512 and max sentence length of 128, let's look at how much the final value in the vector actually changes from the first position, to the 64th position, to the final position. Answer: not much.

print(signal[0, 0, -1])
print(signal[0, 63, -1])
print(signal[0, 127, -1])

tf.Tensor(1.0, shape=(), dtype=float32)
tf.Tensor(0.99998015, shape=(), dtype=float32)
tf.Tensor(0.99991935, shape=(), dtype=float32)

Ditto for a value 16 steps away from the final location:

print(signal[0, 0, -16])
print(signal[0, 63, -16])
print(signal[0, 127, -16])

tf.Tensor(1.0, shape=(), dtype=float32)
tf.Tensor(0.9984067, shape=(), dtype=float32)
tf.Tensor(0.9935305, shape=(), dtype=float32)

I saw elsewhere that BERT's WEs are typically roughly the range of [-2, 2], so adding a 0.007 delta from the PE would not move the WE very much at the -16th position.

So what I think is probably happening is that only ~256 of the PE vector's values are actually moving around as a function of the position... the rest are ~constant. Then the learned WE (Transformers don't use prelearned WE like word2vec or glove), figures out to only use the other ~256 elements. So really... it's conceptually a concat.

notebook here


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    $\begingroup$ Totally agree. The "Attention is All You Need" paper said they tried trainable and fixed PE, and got similar results, but more recent transformers like ViT do train the PE. If both the PE and WE are trainable, then sum is okay, since the PE and WE path can adaptively learn to coexist - conceptually, they can dynamically learn how many elements the PE gets and how many the WE gets. Not unlike a residual skip connection. But if the PE were fixed and very noisy across all elements, the intuition most people have is that it'd be impossible to train a WE or use pretrained WE like word2vec. $\endgroup$
    – Yaoshiang
    Commented Feb 9, 2023 at 20:45
  • $\begingroup$ if this is true, we can basically use half of the embedding vector length then concatenate the half width embedding and positional vector to get exactly same final matrix. $\endgroup$
    – Wang
    Commented Aug 3, 2023 at 15:48
  • $\begingroup$ @Wang, I agree with that directionally. A key would be to understand how to divide up the embedding - doesn't have to be 50/50. Maybe 80/20 or 95/5 ends up being a better use of the vector. A trainable PE would probably be nearly ideal in using the right amount of information. $\endgroup$
    – Yaoshiang
    Commented Aug 25, 2023 at 20:31

The best answer I have seen is this Reddit answer by pappypapaya:

In attention, we basically take two word embeddings (x and y), pass one through a Query transformation matrix (Q) and the second through a Key transformation matrix (K), and compare how similar the resulting query and key vectors are by their dot product. So, basically, we want the dot product between Qx and Ky, which we write as:

(Qx)'(Ky) = x' (Q'Ky).

So equivalently we just need to learn one joint Query-Key transformation (Q'K) that transform the secondary inputs y into a new space in which we can compare x.

By adding positional encodings e and f to x and y, respectively, we essentially change the dot product to

(Q(x+e))' (K(y+f)) = 
(Qx+Qe)' (Ky+Kf) = 
(Qx)' Ky + (Qx)' Kf + (Qe)' Ky + (Qe)' Kf = 
x' (Q'Ky) + x' (Q'Kf) + e' (Q'Ky) + e' (Q'K f) 

where in addition to the original x' (Q'Ky) term, which asks the question "how much attention should we pay to word x given word y", we also have x'(Q'Kf) + e'(Q'Ky) + e'(Q'K f), which ask the additional questions, "how much attention should we pay to word x given the position f of word y", "how much attention should we pay to y given the position e of word x", and "how much attention should we pay to the position e of word x given the position f of word y".

Essentially, the learned transformation matrix Q'K with positional encodings has to do all four of these tasks simultaneously. This is the part that may appear inefficient, since intuitively, there should be a trade-off in the ability of Q'K to do four tasks simultaneously and well.

HOWEVER, MY GUESS is that there isn't actually a trade-off when we force Q'K to do all four of these tasks, because of some approximate orthogonality condition that is satisfied of in high dimensions. The intuition for this is that randomly chosen vectors in high dimensions are almost always approximately orthogonal. There's no reason to think that the word vectors and position encoding vectors are related in any way. If the word embeddings form a smaller dimensional subspace and the positional encodings form another smaller dimensional subspace, then perhaps the two subspaces themselves are approximately orthogonal, so presumably these subspaces can be transformed approx. independently through the same learned Q'K transformation (since they basically exist on different axes in high dimensional space). I don't know if this is true, but it seems intuitively possible.

If true, this would explain why adding positional encodings, instead of concatenation, is essentially fine. Concatenation would ensure that the positional dimensions are orthogonal to the word dimensions, but my guess is that, because these embedding spaces are so high dimensional, you can get approximate orthogonality for free even when adding, without the costs of concatenation (many more parameters to learn). Adding layers would only help with this, by allowing for nonlinearities.

We also ultimately want e and f to behave in some nice ways, so that there's some kind of "closeness" in the vector representation with respect to small changes in positions. The sin and cos representation is nice since nearby positions have high similarity in their positional encodings, which may make it easier to learn transformations that "preserve" this desired closeness.

(Maybe I'm wrong, and the approximate orthogonality arises from stacking multiple layers or non-linearities in the fully-connected parts of the transformer).

tl;dr: It is intuitively possible that, in high dimensions, the word vectors form a smaller dimensional subspace within the full embedding space, and the positional vectors form a different smaller dimensional subspace approximately orthogonal to the one spanned by word vectors. Thus despite vector addition, the two subspaces can be manipulated essentially independently of each other by some single learned transformation. Thus, concatenation doesn't add much, but greatly increases cost in terms of parameters to learn.


It is been a while, but I think anyone ending up here might also be interested in the reading of the following paper:

What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding (Yu-An Wang, Yun-Nung Chen)


I am not changing the accepted answer as this article is not specific.


So the question is about why positional embeddings are directly added to word embeddings instead of concatenated. This is a particularly interesting question. To answer this question, I will need to firstly separate the differences between sequential networks like RNNs and Transformers, which then introduces this problem nicely.

In RNNs, we feed in data (let's say a sequence of words) into the model in a sequential manner. This means that in the context of inputting in a sequence of words, the model does arguably obtain the order the tokens as it is fed in one by one.

With transformers, on the other hand, all of the words in the sequence are fed in all at once. This means that, so far, transformers do not have any notion of word ordering. Therefore, we need positional embeddings to tell the model where each word belongs in the sequence.

I believe the reason why we add them to word embeddings is because we want to maintain a similar input into the model as an RNN, which takes in word embeddings as its input as well. I think your question is a very good one to ask, and maybe you should experiment with having a more compressed word embedding with its positional embedding and compare your approach against the more "traditional" approach and see what results you yield. I'll be excited to see them.


Why everyone compares RNN and Transformers, when you should actually compare Feedforward Neural Networks with Transformers? I am really sorry, I cannot comment @shepan6 answer, so I will post an answer.

This means that, so far, transformers do not have any notion of word ordering. - @shepan6

This is totally wrong and misleading. Transformers are just FNNs. Order of input matter. Please stop spreading disinformation. I know two ablation studies about positional encoding - one in "Attention is all you need" [arxiv:1706.03762] and the other in "Convolutional Sequence to Sequence Learning" [arxiv:1705.03122]. Both authors conclude that there is no or negligible difference in performance of 1) different positional encoding; and 2) present/missing positional encoding.

From paper "Attention is all you need":

We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)).

From paper "Convolutional Sequence to Sequence Learning":

Table 4 shows that position embeddings are helpful but that our model still performs well without them.

  • $\begingroup$ I have just discovered following interesting papers that prove it: arxiv.org/abs/2203.16634 arxiv.org/abs/2305.19466 $\endgroup$ Commented Feb 16 at 14:33
  • $\begingroup$ To quote some paper on positional encoding called "A Simple and Effective Positional Encoding for Transformers", "transformer models are permutation equivariant." This means that the output of a transformer would be the same, regardless of what order the words have, except the last last one, unless we explicitly encode position somehow. Yes, if we reorder the words, the order in which their embeddings are output would change. But those reordered embedding would be exactly the same for their corresponding words. $\endgroup$ Commented Mar 25 at 21:07
  • $\begingroup$ Every operation in the transformer either re-embeds one token based on its own embedding only, or re-embeds a token based upon the values of other tokesn with which it matches. $\endgroup$ Commented Mar 25 at 21:07
  • $\begingroup$ @JosiahYoder That article is correctly saying that the transformer is permutation equivariant on its input. However, please note that LLM's are not permutation equivariant. Ivan provides some links, but his response isn't clear - Multilayer transformer architectures can learn position. LLM's are not just a single transformer. LLM's are 10-100+ layers of transformers. The Layer 2 transformer will utilize the outputs of the Layer 1 transformer. This allows information about position to bleed through. $\endgroup$ Commented May 30 at 17:41
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    $\begingroup$ So, LLM's can learn position, without positional encodings. However, it's similar to "sliding window attention", which solves quadratic attention on context length, by allowing information to slowly bleed through. "sliding window attention" analogously performs poorly. Experimentally, letting information slowly bleed through is bad. It is better to just instantly provide all of the information that it needs. Hence, we use positional embeddings. $\endgroup$ Commented May 30 at 17:43

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