claim

active

claim:naive-interpretation-of-attention-patterns-can-be-both-informative-and-fundamentally-misleading-when-q-k-or-v-composition-is-present

Naive interpretation of attention patterns can be both informative and fundamentally misleading when Q-, K-, or V-composition is present

Response to the 'attention as explanation' critique; the paper provides a typology of when attention is and isn't directly interpretable

Source paper

extracted_from

A Mathematical Framework for Transformer Circuits

(2021) ·

Neighborhood — ranked by edge-count

Papers (1)

paper

A Mathematical Framework for Transformer Circuits
introduces

Claims (1)

claim

Each attention head has two largely independent computations: a QK circuit computing the attention pattern and an OV circuit computing the effect if attended to
supports
Key decomposition enabling separate analysis of where attention goes and what it does

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

In the analyzed two-layer attention-only model, only K-composition is significant; V- and Q-composition are negligible by Frobenius norm measurefinding0.823
Result from applying the Frobenius norm composition measurement to all attention head pairs in the two-layer model
Second-order virtual attention head terms (V-composition) have a small marginal effect in two-layer attention-only modelsclaim0.814
Finding from term importance analysis; allows focus on individual head terms rather than their compositions
Directing response attention to complement syntax and/or mental state verbs (MSV) yields no significant alterations in IIT estimates compared to entire stimulus analysis.finding0.784
Suggests LLMs do not represent complement/MSV linguistic features in the same way as they are crucial for human ToM development.
Even if a case successfully meets all three criteria, this does not necessarily indicate that the corresponding sequence of representations is conscious. Rather, it suggests the observation of a potential 'consciousness' phenomenon within these representations — nothing more.quote0.775
Load-bearing epistemic caution the author places on the entire analytical framework.
The same charitable interpretation must be extended to all systems that display observable response patterns that are consistent with animal cognition, including artificial intelligences, metaplastic materials, and robotic systems.claim0.774
Call to extend the inference of sentience to non-biological systems as well.
Model attention patterns can map to and reveal something about contemplative and flow states.claim0.774
"[W]e must confess that perception, and what depends upon it, is inexplicable in terms of mechanical reasons... when inspecting its interior, we will find only parts that push one another, and we will never find anything to explain a perception."quote0.773
Canonical illustration of the Hard Problem intuition that any functional/mechanical explanation faces an explanatory gap for perception
Attention is a generalization of convolution; all convolutions can be expressed as tensor products of fixed relative position attention patterns and weight matricesclaim0.772
Mathematical equivalence showing the relationship between attention mechanisms and convolutional operations