Zoology: Measuring and Improving Recall in Efficient Language Models
Simran Arora Sabri Eyuboglu Aman Timalsina Isys Johnson Michael Poli James Zou Atri Rudra Christopher Ré
Attention-free language models that combine gating and convolutions are
growing in popularity due to their efficiency and increasingly competitive
performance. To better understand these architectures, we pretrain a suite of
17 attention and "gated-convolution" language models, finding that SoTA
gated-convolution architectures still underperform attention by up to 2.1
perplexity points on the Pile. In fine-grained analysis, we find 82% of the gap
is explained by each model's ability to recall information that is previously
mentioned in-context, e.g. "Hakuna Matata means no worries Hakuna Matata it
means no" $\rightarrow$ "??". On this task, termed "associative recall", we
find that attention outperforms gated-convolutions by a large margin: a 70M
parameter attention model outperforms a 1.4 billion parameter gated-convolution
model on associative recall. This is surprising because prior work shows gated
convolutions can perfectly solve synthetic tests for AR capability. To close
the gap between synthetics and real language, we develop a new formalization of
the task called multi-query associative recall (MQAR) that better reflects
actual language. We perform an empirical and theoretical study of MQAR that
elucidates differences in the parameter-efficiency of attention and
gated-convolution recall. Informed by our analysis, we evaluate simple
convolution-attention hybrids and show that hybrids with input-dependent sparse
attention patterns can close 97.4% of the gap to attention, while maintaining
sub-quadratic scaling. Our code is accessible at:
https://github.com/HazyResearch/zoology.