Differential Transformer

But Figure 1 clearly shows that it works, so I don't doubt that it is in fact possible. I'm just struggling to build a picture of how exactly the network accomplishes this.

I'm a little concerned about the last sentence of the section introduction of "2 Differential Transformer". It mentions using improvements from previous papers, but in the grammatical context, it's unclear if this improvement is added to both the normal transformer and their diff transformer. This would otherwise sully the comparisons. It's the "main difference" wording in the previous sentence that raised a flag for me.

Of course, a good-faith researcher would know this and may not feel the need to clarify. But you can never be too careful about some published research in this field.

The analogy to noise-cancelling headphones is helpful but in that case we clearly know which is signal and which is noise. Here, if we knew why would we even bother to the noise-cancelling work?

If I understand correctly, this architecture trades twice as much attention memory in exchange for either a higher quality model, or less parameters at a similar quality.

> According to the fitted curves, 6.8B-size DIFF Transformer achieves a validation loss comparable to 11B-size Transformer, requiring only 62.2% of parameters

This raises a few questions for me:

- Would having only 60% of the parameters negate the double space for attention, leaving a similar memory profile as a traditional transformer?

- Does that tradeoff change noticeably between training and inference?

I wonder about the story behind that formula...

I'm wondering if there's any effect of "creativity", or ability to interpolate between concepts. Hallucination and creativity feel very related to me. I understand hallucinating as simply being misaligned with the space humans feel appropriate to interpolate between

Crazy gains though congrats to the researchers

It has to be done in a hierarchical way to know what you attended to + full context.

If the differential vector is being computed with the same input as the attention vector how do you know how to modify the attention vector correctly

Simplified differential T. looks like: (softmax(Q₁K₁) − λ softmax(Q₂K₂)) V

You can factor this into:

    x = softmax(Q₁K₁)V
    x += -λ softmax(Q₂K₂)V

Then we would know how much this transformer innovation helps by itself.

I'm imagining a smaller model examining the output tokens of a larger model and metaphorically slapping it on the wrist with a ruler if the output tokens start drifting off topic. Not quite the same, but an entertaining thought nonetheless.

I’m very interested in this claim. I was under the impression that hallucination is unavoidable in these kinds of models. IIRC proof for that was trending on HN a couple weeks ago.

edit: not fully but it gives promising results. quiet an improvement actually.

Of course, even if I'm right proper training would account to that by inverting signs where appropriate. Still, it seems weird to present it as the difference, especially seeing as they compare this directly to noise cancelling headphones, where we sum both microphones inputs.

[...] Specifically, we partition the query and key vectors into two groups and compute two separate softmax attention maps. Then the result of subtracting these two maps is regarded as attention scores.

[...] The approach is analogous to noise-canceling headphones and differential amplifiers in electrical engineering, where the difference between two signals cancels out common-mode noise.

Simple change, with seemingly decent improvements across the board.

"The scaling curves indicate that Diff Transformer requires only about 65% of model size or training tokens needed by Transformer to achieve comparable language modeling performance."

"Diff Transformer retains high performance even at reduced bit-widths, ranging from 16 bits to 6 bits. In comparison, Transformer’s accuracy significantly drops with 6-bit quantization. The 4-bit Diff Transformer achieves comparable accuracy as the 6-bit Transformer, and outperforms the 4-bit Transformer by about 25% in accuracy."