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Auteur principal: Chiang, David
Format: Preprint
Publié: 2024
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Accès en ligne:https://arxiv.org/abs/2409.13629
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author Chiang, David
author_facet Chiang, David
contents Previous work has shown that the languages recognized by average-hard attention transformers (AHATs) and softmax-attention transformers (SMATs) are within the circuit complexity class TC$^0$. However, these results assume limited-precision arithmetic: using floating-point numbers with O(log n) bits (where n is the length of the input string), Strobl showed that AHATs can be approximated in L-uniform TC$^0$, and Merrill and Sabharwal showed that SMATs can be approximated in DLOGTIME-uniform TC$^0$. Here, we improve these results, showing that AHATs with no approximation, SMATs with O(poly(n)) bits of floating-point precision, and SMATs with at most $2^{-O(poly(n))}$ absolute error are all in DLOGTIME-uniform TC$^0$.
format Preprint
id arxiv_https___arxiv_org_abs_2409_13629
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Transformers in Uniform TC$^0$
Chiang, David
Computational Complexity
Formal Languages and Automata Theory
Machine Learning
Previous work has shown that the languages recognized by average-hard attention transformers (AHATs) and softmax-attention transformers (SMATs) are within the circuit complexity class TC$^0$. However, these results assume limited-precision arithmetic: using floating-point numbers with O(log n) bits (where n is the length of the input string), Strobl showed that AHATs can be approximated in L-uniform TC$^0$, and Merrill and Sabharwal showed that SMATs can be approximated in DLOGTIME-uniform TC$^0$. Here, we improve these results, showing that AHATs with no approximation, SMATs with O(poly(n)) bits of floating-point precision, and SMATs with at most $2^{-O(poly(n))}$ absolute error are all in DLOGTIME-uniform TC$^0$.
title Transformers in Uniform TC$^0$
topic Computational Complexity
Formal Languages and Automata Theory
Machine Learning
url https://arxiv.org/abs/2409.13629