Exploring Causal Attention in Transformers

Federico Barbero (DeepMind/Oxford) is the lead author of "Transformers Need Glasses!".

Have you ever wondered why LLMs struggle with seemingly simple tasks like counting or copying long strings of text? We break down the theoretical reasons behind these failures, revealing architectural bottlenecks and the challenges of maintaining information fidelity across extended contexts.

Federico explains how these issues are rooted in the transformer's design, drawing parallels to over-squashing in graph neural networks and detailing how the softmax function limits sharp decision-making.

But it's not all bad news! Discover practical "glasses" that can help transformers see more clearly, from simple input modifications to architectural tweaks.

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https://federicobarbero.com/

TRANSCRIPT + RESEARCH:

https://www.dropbox.com/s/h7ys83ztwktqjje/Federico.pdf?dl=0

TOC:

1. Transformer Limitations: Token Detection & Representation

[00:00:00] 1.1 Transformers fail at single token detection

[00:02:45] 1.2 Representation collapse in transformers

[00:03:21] 1.3 Experiment: LLMs fail at copying last tokens

[00:18:00] 1.4 Attention sharpness limitations in transformers

2. Transformer Limitations: Information Flow & Quantization

[00:18:50] 2.1 Unidirectional information mixing

[00:18:50] 2.2 Unidirectional information flow towards sequence beginning in transformers

[00:21:50] 2.3 Diagonal attention heads as expensive no-ops in LAMA/Gemma

[00:27:14] 2.4 Sequence entropy affects transformer model distinguishability

[00:30:36] 2.5 Quantization limitations lead to information loss & representational collapse

[00:38:34] 2.6 LLMs use subitizing as opposed to counting algorithms

3. Transformers and the Nature of Reasoning

[00:40:30] 3.1 Turing completeness conditions in transformers

[00:43:23] 3.2 Transformers struggle with sequential tasks

[00:45:50] 3.3 Windowed attention as solution to information compression

[00:51:04] 3.4 Chess engines: mechanical computation vs creative reasoning

[01:00:35] 3.5 Epistemic foraging introduced

REFS:

[00:01:05] Transformers Need Glasses!, Barbero et al.

https://proceedings.neurips.cc/paper_files/paper/2024/file/b1d35561c4a4a0e0b6012b2af531e149-Paper-Conference.pdf

[00:05:30] Softmax is Not Enough, Veličković et al.

https://arxiv.org/abs/2410.01104

[00:11:30] Adv Alg Lecture 15, Chawla

https://pages.cs.wisc.edu/~shuchi/courses/787-F09/scribe-notes/lec15.pdf

[00:15:05] Graph Attention Networks, Veličković

https://arxiv.org/abs/1710.10903

[00:19:15] Extract Training Data, Carlini et al.

https://arxiv.org/pdf/2311.17035

[00:31:30] 1-bit LLMs, Ma et al.

https://arxiv.org/abs/2402.17764

[00:38:35] LLMs Solve Math, Nikankin et al.

https://arxiv.org/html/2410.21272v1

[00:38:45] Subitizing, Railo

https://link.springer.com/10.1007/978-1-4419-1428-6_578

[00:43:25] NN & Chomsky Hierarchy, Delétang et al.

https://arxiv.org/abs/2207.02098

[00:51:05] Measure of Intelligence, Chollet

https://arxiv.org/abs/1911.01547

[00:52:10] AlphaZero, Silver et al.

https://pubmed.ncbi.nlm.nih.gov/30523106/

[00:55:10] Golden Gate Claude, Anthropic

https://www.anthropic.com/news/golden-gate-claude

[00:56:40] Chess Positions, Chase & Simon

https://www.sciencedirect.com/science/article/abs/pii/0010028573900042

[01:00:35] Epistemic Foraging, Friston

https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2016.00056/full